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2020 – today
- 2024
[j23]Noriaki Hirose
, Dhruv Shah, Ajay Sridhar, Sergey Levine:
SACSoN: Scalable Autonomous Control for Social Navigation. IEEE Robotics Autom. Lett. 9(1): 49-56 (2024)- [j22]Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng, Kuan Fang, Liam Tan, Stefan Schaal, Sergey Levine:
Multistage Cable Routing Through Hierarchical Imitation Learning. IEEE Trans. Robotics 40: 1476-1491 (2024) - [c403]Kuba Grudzien Kuba, Masatoshi Uehara, Sergey Levine, Pieter Abbeel:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. AISTATS 2024: 2449-2457 - [c402]Marwa Abdulhai, Micah Carroll, Justin Svegliato, Anca D. Dragan, Sergey Levine:
Defining Deception in Decision Making. AAMAS 2024: 2111-2113 - [c401]Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, Sergey Levine:
Training Diffusion Models with Reinforcement Learning. ICLR 2024 - [c400]Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Rich Walke, Chelsea Finn, Aviral Kumar, Sergey Levine:
Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models. ICLR 2024 - [c399]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. ICLR 2024 - [c398]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary Language Models. ICLR 2024 - [c397]Joey Hong, Anca D. Dragan, Sergey Levine:
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity. ICLR 2024 - [c396]Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine:
Deep Neural Networks Tend To Extrapolate Predictably. ICLR 2024 - [c395]Jianlan Luo, Perry Dong, Yuexiang Zhai, Yi Ma, Sergey Levine:
RLIF: Interactive Imitation Learning as Reinforcement Learning. ICLR 2024 - [c394]Seohong Park, Oleh Rybkin, Sergey Levine:
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. ICLR 2024 - [c393]Chongyi Zheng, Benjamin Eysenbach, Homer Rich Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine:
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data. ICLR 2024 - [c392]Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter:
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator. ICML 2024 - [c391]Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. ICML 2024 - [c390]Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine:
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. ICML 2024 - [c389]Vivek Myers, Chongyi Zheng, Anca D. Dragan, Sergey Levine, Benjamin Eysenbach:
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making. ICML 2024 - [c388]Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter:
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. ICML 2024 - [c387]Seohong Park, Tobias Kreiman, Sergey Levine:
Foundation Policies with Hilbert Representations. ICML 2024 - [c386]Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine:
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML 2024 - [c385]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. ICML 2024 - [c384]Annie Xie, Logan M. Bhamidipaty, Evan Zheran Liu, Joey Hong, Sergey Levine, Chelsea Finn:
Learning to Explore in POMDPs with Informational Rewards. ICML 2024 - [c383]Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar:
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. ICML 2024 - [c382]Ajay Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine:
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. ICRA 2024: 63-70 - [c381]Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su, Haoshu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi Jim Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Lin:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. ICRA 2024: 6892-6903 - [c380]Laura M. Smith, Yunhao Cao, Sergey Levine:
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion. ICRA 2024: 10829-10836 - [c379]Jianlan Luo, Zheyuan Hu, Charles Xu, You Liang Tan, Jacob Berg, Archit Sharma, Stefan Schaal, Chelsea Finn, Abhishek Gupta, Sergey Levine:
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning. ICRA 2024: 16961-16969 - [c378]Chethan Bhateja, Derek Guo, Dibya Ghosh, Anikait Singh, Manan Tomar, Quan Vuong, Yevgen Chebotar, Sergey Levine, Aviral Kumar:
Robotic Offline RL from Internet Videos via Value-Function Learning. ICRA 2024: 16977-16984 - [c377]Rafael Rafailov, Kyle Beltran Hatch, Anikait Singh, Aviral Kumar, Laura M. Smith, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J. Ball, Jiajun Wu, Sergey Levine, Chelsea Finn:
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. RLC 2024: 2178-2197 - [i474]Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. CoRR abs/2401.05442 (2024) - [i473]Jianlan Luo, Charles Xu, Fangchen Liu, Liam Tan, Zipeng Lin, Jeffrey Wu, Pieter Abbeel, Sergey Levine:
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning. CoRR abs/2401.08553 (2024) - [i472]Michael Ahn, Debidatta Dwibedi, Chelsea Finn, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Karol Hausman, Brian Ichter, Alex Irpan, Nikhil J. Joshi, Ryan Julian, Sean Kirmani, Isabel Leal, Tsang-Wei Edward Lee, Sergey Levine, Yao Lu, Sharath Maddineni, Kanishka Rao, Dorsa Sadigh, Pannag Sanketi, Pierre Sermanet, Quan Vuong, Stefan Welker, Fei Xia, Ted Xiao, Peng Xu, Steve Xu, Zhuo Xu:
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents. CoRR abs/2401.12963 (2024) - [i471]Jianlan Luo, Zheyuan Hu, Charles Xu, You Liang Tan, Jacob Berg, Archit Sharma, Stefan Schaal, Chelsea Finn, Abhishek Gupta, Sergey Levine:
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning. CoRR abs/2401.16013 (2024) - [i470]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control. CoRR abs/2401.16889 (2024) - [i469]William Chen, Oier Mees, Aviral Kumar, Sergey Levine:
Vision-Language Models Provide Promptable Representations for Reinforcement Learning. CoRR abs/2402.02651 (2024) - [i468]Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter:
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. CoRR abs/2402.07872 (2024) - [i467]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Tommaso Biancalani, Sergey Levine:
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. CoRR abs/2402.15194 (2024) - [i466]Seohong Park, Tobias Kreiman, Sergey Levine:
Foundation Policies with Hilbert Representations. CoRR abs/2402.15567 (2024) - [i465]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. CoRR abs/2402.16359 (2024) - [i464]Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine:
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. CoRR abs/2402.17135 (2024) - [i463]Jonathan Yang, Catherine Glossop, Arjun Bhorkar, Dhruv Shah, Quan Vuong, Chelsea Finn, Dorsa Sadigh, Sergey Levine:
Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation. CoRR abs/2402.19432 (2024) - [i462]Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar:
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. CoRR abs/2402.19446 (2024) - [i461]Noriaki Hirose, Dhruv Shah, Kyle Stachowicz, Ajay Sridhar, Sergey Levine:
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation. CoRR abs/2403.00991 (2024) - [i460]Fangchen Liu, Kuan Fang, Pieter Abbeel, Sergey Levine:
MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting. CoRR abs/2403.03174 (2024) - [i459]Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. CoRR abs/2403.03950 (2024) - [i458]Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine:
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference. CoRR abs/2403.04082 (2024) - [i457]Katie Kang, Eric Wallace, Claire J. Tomlin, Aviral Kumar, Sergey Levine:
Unfamiliar Finetuning Examples Control How Language Models Hallucinate. CoRR abs/2403.05612 (2024) - [i456]Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma, Karl Pertsch, Jianlan Luo, Sergey Levine, Chelsea Finn:
Yell At Your Robot: Improving On-the-Fly from Language Corrections. CoRR abs/2403.12910 (2024) - [i455]Jiayi Pan, Yichi Zhang, Nicholas Tomlin, Yifei Zhou, Sergey Levine, Alane Suhr:
Autonomous Evaluation and Refinement of Digital Agents. CoRR abs/2404.06474 (2024) - [i454]Toru Lin, Yu Zhang, Qiyang Li, Haozhi Qi, Brent Yi, Sergey Levine, Jitendra Malik:
Learning Visuotactile Skills with Two Multifingered Hands. CoRR abs/2404.16823 (2024) - [i453]Kyle Stachowicz, Sergey Levine:
RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes. CoRR abs/2405.04714 (2024) - [i452]Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, Ted Xiao:
Evaluating Real-World Robot Manipulation Policies in Simulation. CoRR abs/2405.05941 (2024) - [i451]Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, Sergey Levine:
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning. CoRR abs/2405.10292 (2024) - [i450]Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, Sergey Levine:
Octo: An Open-Source Generalist Robot Policy. CoRR abs/2405.12213 (2024) - [i449]Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali, Gabriele Scalia, Gökcen Eraslan, Avantika Lal, Sergey Levine, Tommaso Biancalani:
Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. CoRR abs/2405.19673 (2024) - [i448]Yutaka Shimizu, Joey Hong, Sergey Levine, Masayoshi Tomizuka:
Strategically Conservative Q-Learning. CoRR abs/2406.04534 (2024) - [i447]Seungeun Rho, Laura M. Smith, Tianyu Li, Sergey Levine, Xue Bin Peng, Sehoon Ha:
Language Guided Skill Discovery. CoRR abs/2406.06615 (2024) - [i446]Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Paul Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn:
OpenVLA: An Open-Source Vision-Language-Action Model. CoRR abs/2406.09246 (2024) - [i445]Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar:
Is Value Learning Really the Main Bottleneck in Offline RL? CoRR abs/2406.09329 (2024) - [i444]Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, Aviral Kumar:
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning. CoRR abs/2406.11896 (2024) - [i443]Yulai Zhao, Masatoshi Uehara, Gabriele Scalia, Tommaso Biancalani, Sergey Levine, Ehsan Hajiramezanali:
Adding Conditional Control to Diffusion Models with Reinforcement Learning. CoRR abs/2406.12120 (2024) - [i442]Vivek Myers, Chongyi Zheng, Anca D. Dragan, Sergey Levine, Benjamin Eysenbach:
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making. CoRR abs/2406.17098 (2024) - [i441]Annie S. Chen, Alec M. Lessing, Andy Tang, Govind Chada, Laura M. Smith, Sergey Levine, Chelsea Finn:
Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models. CoRR abs/2407.02666 (2024) - [i440]Xiaoyu Huang, Qiayuan Liao, Yiming Ni, Zhongyu Li, Laura M. Smith, Sergey Levine, Xue Bin Peng, Koushil Sreenath:
HiLMa-Res: A General Hierarchical Framework via Residual RL for Combining Quadrupedal Locomotion and Manipulation. CoRR abs/2407.06584 (2024) - [i439]Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee, Wenhao Yu, Connor Schenck, David Rendleman, Dhruv Shah, Fei Xia, Jasmine Hsu, Jonathan Hoech, Pete Florence, Sean Kirmani, Sumeet Singh, Vikas Sindhwani, Carolina Parada, Chelsea Finn, Peng Xu, Sergey Levine, Jie Tan:
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs. CoRR abs/2407.07775 (2024) - [i438]Michal Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, Sergey Levine:
Robotic Control via Embodied Chain-of-Thought Reasoning. CoRR abs/2407.08693 (2024) - [i437]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024) - [i436]Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine:
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. CoRR abs/2407.13734 (2024) - [i435]Zhiyuan Zhou, Pranav Atreya, Abraham Lee, Homer Walke, Oier Mees, Sergey Levine:
Autonomous Improvement of Instruction Following Skills via Foundation Models. CoRR abs/2407.20635 (2024) - [i434]Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gökcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, Masatoshi Uehara:
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. CoRR abs/2408.08252 (2024) - [i433]Rafael Rafailov, Kyle Hatch, Anikait Singh, Laura M. Smith, Aviral Kumar, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J. Ball, Jiajun Wu, Chelsea Finn, Sergey Levine:
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. CoRR abs/2408.08441 (2024) - [i432]Ria Doshi, Homer Walke, Oier Mees, Sudeep Dasari, Sergey Levine:
Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation. CoRR abs/2408.11812 (2024) - [i431]Junsu Kim, Seohong Park, Sergey Levine:
Unsupervised-to-Online Reinforcement Learning. CoRR abs/2408.14785 (2024) - [i430]Vivek Myers, Bill Chunyuan Zheng, Oier Mees, Sergey Levine, Kuan Fang:
Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation. CoRR abs/2408.16228 (2024) - [i429]Grace Tang, Swetha Rajkumar, Yifei Zhou, Homer Rich Walke, Sergey Levine, Kuan Fang:
KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data. CoRR abs/2409.14066 (2024) - [i428]Noriaki Hirose, Catherine Glossop, Ajay Sridhar, Dhruv Shah, Oier Mees, Sergey Levine:
LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Videos. CoRR abs/2410.03603 (2024) - 2023
- [j21]Shagun Sodhani, Sergey Levine, Amy Zhang:
Improving Generalization with Approximate Factored Value Functions. Trans. Mach. Learn. Res. 2023 (2023) - [c376]Dhruv Shah, Ajay Sridhar, Nitish Dashora, Kyle Stachowicz, Kevin Black, Noriaki Hirose, Sergey Levine:
ViNT: A Foundation Model for Visual Navigation. CoRL 2023: 711-733 - [c375]Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar, Xinyang Geng, Sergey Levine:
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning. CoRL 2023: 1348-1361 - [c374]Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, Sergey Levine:
BridgeData V2: A Dataset for Robot Learning at Scale. CoRL 2023: 1723-1736 - [c373]Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, Sergey Levine:
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation. CoRL 2023: 1930-1949 - [c372]Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, Quan Vuong, Vincent Vanhoucke, Huong T. Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R. Sanketi, Grecia Salazar, Michael S. Ryoo, Krista Reymann, Kanishka Rao, Karl Pertsch, Igor Mordatch, Henryk Michalewski, Yao Lu, Sergey Levine, Lisa Lee, Tsang-Wei Edward Lee, Isabel Leal, Yuheng Kuang, Dmitry Kalashnikov, Ryan Julian, Nikhil J. Joshi, Alex Irpan, Brian Ichter, Jasmine Hsu, Alexander Herzog, Karol Hausman, Keerthana Gopalakrishnan, Chuyuan Fu, Pete Florence, Chelsea Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, Kehang Han:
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. CoRL 2023: 2165-2183 - [c371]Dhruv Shah, Michael Robert Equi, Blazej Osinski, Fei Xia, Brian Ichter, Sergey Levine:
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning. CoRL 2023: 2683-2699 - [c370]Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov, Sergey Levine:
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing. CoRL 2023: 3100-3111 - [c369]Vivek Myers, Andre Wang He, Kuan Fang, Homer Rich Walke, Philippe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca D. Dragan, Sergey Levine:
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control. CoRL 2023: 3894-3908 - [c368]Yevgen Chebotar, Quan Vuong, Karol Hausman, Fei Xia, Yao Lu, Alex Irpan, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Anand Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine:
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. CoRL 2023: 3909-3928 - [c367]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR 2023 - [c366]Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Russ Salakhutdinov:
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. ICLR 2023 - [c365]Joey Hong, Aviral Kumar, Sergey Levine:
Confidence-Conditioned Value Functions for Offline Reinforcement Learning. ICLR 2023 - [c364]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR 2023 - [c363]Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine:
Efficient Deep Reinforcement Learning Requires Regulating Overfitting. ICLR 2023 - [c362]Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine:
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR 2023 - [c361]Charlie Snell, Ilya Kostrikov, Yi Su, Sherry Yang, Sergey Levine:
Offline RL for Natural Language Generation with Implicit Language Q Learning. ICLR 2023 - [c360]Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine:
Efficient Online Reinforcement Learning with Offline Data. ICML 2023: 1577-1594 - [c359]Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. ICML 2023: 8469-8488 - [c358]Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. ICML 2023: 9485-9507 - [c357]Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine:
Reinforcement Learning from Passive Data via Latent Intentions. ICML 2023: 11321-11339 - [c356]Qiyang Li, Yuexiang Zhai, Yi Ma, Sergey Levine:
Understanding the Complexity Gains of Single-Task RL with a Curriculum. ICML 2023: 20412-20451 - [c355]Seohong Park, Sergey Levine:
Predictable MDP Abstraction for Unsupervised Model-Based RL. ICML 2023: 27246-27268 - [c354]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. ICML 2023: 34556-34583 - [c353]Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Superhuman Go AIs. ICML 2023: 35655-35739 - [c352]Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine:
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation. ICRA 2023: 4077-4084 - [c351]Abhishek Gupta, Corey Lynch, Brandon Kinman, Garrett Peake, Sergey Levine, Karol Hausman:
Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning. ICRA 2023: 5020-5026 - [c350]Kelvin Xu, Zheyuan Hu, Ria Doshi, Aaron Rovinsky, Vikash Kumar, Abhishek Gupta, Sergey Levine:
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance. ICRA 2023: 5938-5945 - [c349]Ashvin Nair, Brian Zhu, Gokul Narayanan, Eugen Solowjow, Sergey Levine:
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision. ICRA 2023: 7154-7161 - [c348]Dhruv Shah, Ajay Sridhar, Arjun Bhorkar, Noriaki Hirose, Sergey Levine:
GNM: A General Navigation Model to Drive Any Robot. ICRA 2023: 7226-7233 - [c347]Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine:
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning. IROS 2023: 7523-7530 - [c346]Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine:
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. IROS 2023: 7553-7560 - [c345]Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn:
Contrastive Example-Based Control. L4DC 2023: 155-169 - [c344]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. L4DC 2023: 586-599 - [c343]Joey Hong, Sergey Levine, Anca D. Dragan:
Learning to Influence Human Behavior with Offline Reinforcement Learning. NeurIPS 2023 - [c342]Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, Brian Ichter:
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents. NeurIPS 2023 - [c341]Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine:
Accelerating Exploration with Unlabeled Prior Data. NeurIPS 2023 - [c340]Mitsuhiko Nakamoto, Simon Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine:
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. NeurIPS 2023 - [c339]Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine:
HIQL: Offline Goal-Conditioned RL with Latent States as Actions. NeurIPS 2023 - [c338]Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, Sergey Levine:
ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints. NeurIPS 2023 - [c337]Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman:
Ignorance is Bliss: Robust Control via Information Gating. NeurIPS 2023 - [c336]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag R. Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-1: Robotics Transformer for Real-World Control at Scale. Robotics: Science and Systems 2023 - [c335]Alexander Herzog, Kanishka Rao, Karol Hausman, Yao Lu, Paul Wohlhart, Mengyuan Yan, Jessica Lin, Montserrat Gonzalez Arenas, Ted Xiao, Daniel Kappler, Daniel Ho, Jarek Rettinghouse, Yevgen Chebotar, Kuang-Huei Lee, Keerthana Gopalakrishnan, Ryan Julian, Adrian Li, Chuyuan Fu, Bob Wei, Sangeetha Ramesh, Khem Holden, Kim Kleiven, David J. Rendleman, Sean Kirmani, Jeffrey Bingham, Jonathan Weisz, Ying Xu, Wenlong Lu, Matthew Bennice, Cody Fong, David Do, Jessica Lam, Yunfei Bai, Benjie Holson, Michael Quinlan, Noah Brown, Mrinal Kalakrishnan, Julian Ibarz, Peter Pastor, Sergey Levine:
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators. Robotics: Science and Systems 2023 - [c334]Ilya Kostrikov, Laura M. Smith, Sergey Levine:
Demonstrating A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning. Robotics: Science and Systems 2023 - [c333]Aviral Kumar, Anikait Singh, Frederik D. Ebert, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, Sergey Levine:
Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials. Robotics: Science and Systems 2023 - [c332]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning. Robotics: Science and Systems 2023 - [c331]Laura M. Smith, J. Chase Kew, Tianyu Li, Linda Luu, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine:
Learning and Adapting Agile Locomotion Skills by Transferring Experience. Robotics: Science and Systems 2023 - [c330]Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid, Anthony Brohan, Karol Hausman, Sergey Levine, Jonathan Tompson:
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models. Robotics: Science and Systems 2023 - [c329]Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn:
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. Robotics: Science and Systems 2023 - [e1]Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine:
Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 2023 [contents] - [i427]Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine:
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR abs/2302.02931 (2023) - [i426]Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine:
Efficient Online Reinforcement Learning with Offline Data. CoRR abs/2302.02948 (2023) - [i425]Seohong Park, Sergey Levine:
Predictable MDP Abstraction for Unsupervised Model-Based RL. CoRR abs/2302.03921 (2023) - [i424]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. CoRR abs/2302.05441 (2023) - [i423]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning. CoRR abs/2302.09450 (2023) - [i422]Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, Brian Ichter:
Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control. CoRR abs/2303.00855 (2023) - [i421]Joey Hong, Anca D. Dragan, Sergey Levine:
Learning to Influence Human Behavior with Offline Reinforcement Learning. CoRR abs/2303.02265 (2023) - [i420]Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. CoRR abs/2303.03378 (2023) - [i419]Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine:
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. CoRR abs/2303.05479 (2023) - [i418]Manan Tomar, Riashat Islam, Sergey Levine, Philip Bachman:
Ignorance is Bliss: Robust Control via Information Gating. CoRR abs/2303.06121 (2023) - [i417]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR abs/2303.11373 (2023) - [i416]Dibya Ghosh, Chethan Bhateja, Sergey Levine:
Reinforcement Learning from Passive Data via Latent Intentions. CoRR abs/2304.04782 (2023) - [i415]Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov, Sergey Levine:
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing. CoRR abs/2304.09831 (2023) - [i414]Laura M. Smith, J. Chase Kew, Tianyu Li, Linda Luu, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine:
Learning and Adapting Agile Locomotion Skills by Transferring Experience. CoRR abs/2304.09834 (2023) - [i413]Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine:
Efficient Deep Reinforcement Learning Requires Regulating Overfitting. CoRR abs/2304.10466 (2023) - [i412]Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, Sergey Levine:
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies. CoRR abs/2304.10573 (2023) - [i411]Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn:
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. CoRR abs/2304.13705 (2023) - [i410]Alexander Herzog, Kanishka Rao, Karol Hausman, Yao Lu, Paul Wohlhart, Mengyuan Yan, Jessica Lin, Montserrat Gonzalez Arenas, Ted Xiao, Daniel Kappler, Daniel Ho, Jarek Rettinghouse, Yevgen Chebotar, Kuang-Huei Lee, Keerthana Gopalakrishnan, Ryan Julian, Adrian Li, Chuyuan Kelly Fu, Bob Wei, Sangeetha Ramesh, Khem Holden, Kim Kleiven, David Rendleman, Sean Kirmani, Jeff Bingham, Jonathan Weisz, Ying Xu, Wenlong Lu, Matthew Bennice, Cody Fong, David Do, Jessica Lam, Yunfei Bai, Benjie Holson, Michael Quinlan, Noah Brown, Mrinal Kalakrishnan, Julian Ibarz, Peter Pastor, Sergey Levine:
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators. CoRR abs/2305.03270 (2023) - [i409]Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, Sergey Levine:
Training Diffusion Models with Reinforcement Learning. CoRR abs/2305.13301 (2023) - [i408]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary LLMs. CoRR abs/2305.15717 (2023) - [i407]Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine:
SACSoN: Scalable Autonomous Data Collection for Social Navigation. CoRR abs/2306.01874 (2023) - [i406]Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine:
Stabilizing Contrastive RL: Techniques for Offline Goal Reaching. CoRR abs/2306.03346 (2023) - [i405]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. CoRR abs/2306.11120 (2023) - [i404]Dhruv Shah, Ajay Sridhar, Nitish Dashora, Kyle Stachowicz, Kevin Black, Noriaki Hirose, Sergey Levine:
ViNT: A Foundation Model for Visual Navigation. CoRR abs/2306.14846 (2023) - [i403]Vivek Myers, Andre He, Kuan Fang, Homer Walke, Philippe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca D. Dragan, Sergey Levine:
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control. CoRR abs/2307.00117 (2023) - [i402]Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng, Kuan Fang, Liam Tan, Stefan Schaal, Sergey Levine:
Multi-Stage Cable Routing through Hierarchical Imitation Learning. CoRR abs/2307.08927 (2023) - [i401]Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine:
HIQL: Offline Goal-Conditioned RL with Latent States as Actions. CoRR abs/2307.11949 (2023) - [i400]Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning. CoRR abs/2307.12968 (2023) - [i399]Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn:
Contrastive Example-Based Control. CoRR abs/2307.13101 (2023) - [i398]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Lisa Lee, Tsang-Wei Edward Lee, Sergey Levine, Yao Lu, Henryk Michalewski, Igor Mordatch, Karl Pertsch, Kanishka Rao, Krista Reymann, Michael S. Ryoo, Grecia Salazar, Pannag Sanketi, Pierre Sermanet, Jaspiar Singh, Anikait Singh, Radu Soricut, Huong T. Tran, Vincent Vanhoucke, Quan Vuong, Ayzaan Wahid, Stefan Welker, Paul Wohlhart, Jialin Wu, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. CoRR abs/2307.15818 (2023) - [i397]Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Maximilian Du, Chongyi Zheng, Tony Z. Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, Sergey Levine:
BridgeData V2: A Dataset for Robot Learning at Scale. CoRR abs/2308.12952 (2023) - [i396]Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, Sergey Levine:
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation. CoRR abs/2309.03322 (2023) - [i395]Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine:
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning. CoRR abs/2309.03839 (2023) - [i394]Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine:
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. CoRR abs/2309.10150 (2023) - [i393]Chethan Bhateja, Derek Guo, Dibya Ghosh, Anikait Singh, Manan Tomar, Quan Vuong, Yevgen Chebotar, Sergey Levine, Aviral Kumar:
Robotic Offline RL from Internet Videos via Value-Function Pre-Training. CoRR abs/2309.13041 (2023) - [i392]Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine:
Deep Neural Networks Tend To Extrapolate Predictably. CoRR abs/2310.00873 (2023) - [i391]Ajay Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine:
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. CoRR abs/2310.07896 (2023) - [i390]Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn:
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias. CoRR abs/2310.08558 (2023) - [i389]Seohong Park, Oleh Rybkin, Sergey Levine:
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. CoRR abs/2310.08887 (2023) - [i388]Han Qi, Xinyang Geng, Stefano Rando, Iku Ohama, Aviral Kumar, Sergey Levine:
Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction. CoRR abs/2310.10056 (2023) - [i387]Dhruv Shah, Michael Equi, Blazej Osinski, Fei Xia, Brian Ichter, Sergey Levine:
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning. CoRR abs/2310.10103 (2023) - [i386]Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, Sergey Levine:
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models. CoRR abs/2310.10639 (2023) - [i385]Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar, Xinyang Geng, Sergey Levine:
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning. CoRR abs/2310.11731 (2023) - [i384]Laura M. Smith, Yunhao Cao, Sergey Levine:
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion. CoRR abs/2310.17634 (2023) - [i383]Joey Hong, Anca D. Dragan, Sergey Levine:
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity. CoRR abs/2310.20663 (2023) - [i382]Annie S. Chen, Govind Chada, Laura M. Smith, Archit Sharma, Zipeng Fu, Sergey Levine, Chelsea Finn:
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment. CoRR abs/2311.01059 (2023) - [i381]Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine:
Accelerating Exploration with Unlabeled Prior Data. CoRR abs/2311.05067 (2023) - [i380]Joey Hong, Sergey Levine, Anca D. Dragan:
Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations. CoRR abs/2311.05584 (2023) - [i379]Jianlan Luo, Perry Dong, Yuexiang Zhai, Yi Ma, Sergey Levine:
RLIF: Interactive Imitation Learning as Reinforcement Learning. CoRR abs/2311.12996 (2023) - [i378]Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine:
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models. CoRR abs/2311.18232 (2023) - [i377]Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter:
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator. CoRR abs/2312.04474 (2023) - 2022
- [j20]Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, Sanja Fidler:
ASE: large-scale reusable adversarial skill embeddings for physically simulated characters. ACM Trans. Graph. 41(4): 94:1-94:17 (2022) - [c328]Dhruv Shah, Arjun Bhorkar, Hrishit Leen, Ilya Kostrikov, Nicholas Rhinehart, Sergey Levine:
Offline Reinforcement Learning for Visual Navigation. CoRL 2022: 44-54 - [c327]Kuan Fang, Patrick Yin, Ashvin Nair, Homer Walke, Gengchen Yan, Sergey Levine:
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks. CoRL 2022: 106-117 - [c326]Brian Ichter, Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, Dmitry Kalashnikov, Sergey Levine, Yao Lu, Carolina Parada, Kanishka Rao, Pierre Sermanet, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Mengyuan Yan, Noah Brown, Michael Ahn, Omar Cortes, Nicolas Sievers, Clayton Tan, Sichun Xu, Diego Reyes, Jarek Rettinghouse, Jornell Quiambao, Peter Pastor, Linda Luu, Kuang-Huei Lee, Yuheng Kuang, Sally Jesmonth, Nikhil J. Joshi, Kyle Jeffrey, Rosario Jauregui Ruano, Jasmine Hsu, Keerthana Gopalakrishnan, Byron David, Andy Zeng, Chuyuan Kelly Fu:
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. CoRL 2022: 287-318 - [c325]Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine:
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action. CoRL 2022: 492-504 - [c324]Charles Packer, Nicholas Rhinehart, Rowan Thomas McAllister, Matthew A. Wright, Xin Wang, Jeff He, Sergey Levine, Joseph E. Gonzalez:
Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty. CoRL 2022: 1607-1617 - [c323]Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine:
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. CoRL 2022: 1652-1662 - [c322]Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Tomas Jackson, Noah Brown, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter:
Inner Monologue: Embodied Reasoning through Planning with Language Models. CoRL 2022: 1769-1782 - [c321]Gilbert Feng, Hongbo Zhang, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, Sergey Levine:
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots. CoRL 2022: 1893-1903 - [c320]Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine:
CoMPS: Continual Meta Policy Search. ICLR 2022 - [c319]Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine:
Information Prioritization through Empowerment in Visual Model-based RL. ICLR 2022 - [c318]Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine:
RvS: What is Essential for Offline RL via Supervised Learning? ICLR 2022 - [c317]Benjamin Eysenbach, Sergey Levine:
Maximum Entropy RL (Provably) Solves Some Robust RL Problems. ICLR 2022 - [c316]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
The Information Geometry of Unsupervised Reinforcement Learning. ICLR 2022 - [c315]Ilya Kostrikov, Ashvin Nair, Sergey Levine:
Offline Reinforcement Learning with Implicit Q-Learning. ICLR 2022 - [c314]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR 2022 - [c313]Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine:
Should I Run Offline Reinforcement Learning or Behavioral Cloning? ICLR 2022 - [c312]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR 2022 - [c311]Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang:
Extending the WILDS Benchmark for Unsupervised Adaptation. ICLR 2022 - [c310]Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter:
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning. ICLR 2022 - [c309]Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn:
Autonomous Reinforcement Learning: Formalism and Benchmarking. ICLR 2022 - [c308]Mengjiao Yang, Sergey Levine, Ofir Nachum:
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data. ICLR 2022 - [c307]Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez:
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks. ICLR 2022 - [c306]Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine:
Offline RL Policies Should Be Trained to be Adaptive. ICML 2022: 7513-7530 - [c305]Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine:
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. ICML 2022: 8407-8426 - [c304]Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine:
Planning with Diffusion for Flexible Behavior Synthesis. ICML 2022: 9902-9915 - [c303]Katie Kang, Paula Gradu, Jason J. Choi, Michael Janner, Claire J. Tomlin, Sergey Levine:
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control. ICML 2022: 10708-10733 - [c302]Vitchyr H. Pong, Ashvin Nair, Laura M. Smith, Catherine Huang, Sergey Levine:
Offline Meta-Reinforcement Learning with Online Self-Supervision. ICML 2022: 17811-17829 - [c301]Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine:
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. ICML 2022: 21658-21676 - [c300]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. ICML 2022: 25611-25635 - [c299]Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon:
Control-Aware Prediction Objectives for Autonomous Driving. ICRA 2022: 1-8 - [c298]Laura M. Smith, J. Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine:
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World. ICRA 2022: 1593-1599 - [c297]Nitish Dashora, Daniel Shin, Dhruv Shah, Henry A. Leopold, David D. Fan, Ali-Akbar Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine:
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments. ICRA 2022: 4452-4458 - [c296]Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, Sergey Levine:
Offline Meta-Reinforcement Learning for Industrial Insertion. ICRA 2022: 6386-6393 - [c295]Sean Chen, Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine:
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning. ICRA 2022: 7505-7512 - [c294]Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath:
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot. IROS 2022: 1479-1486 - [c293]Kuan Fang, Patrick Yin, Ashvin Nair, Sergey Levine:
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space. IROS 2022: 4076-4083 - [c292]Charlie Snell, Sherry Yang, Justin Fu, Yi Su, Sergey Levine:
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. NAACL-HLT (Findings) 2022: 2351-2366 - [c291]Siddharth Verma, Justin Fu, Sherry Yang, Sergey Levine:
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning. NAACL-HLT 2022: 4471-4491 - [c290]Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS 2022 - [c289]Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine:
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. NeurIPS 2022 - [c288]Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal:
Distributionally Adaptive Meta Reinforcement Learning. NeurIPS 2022 - [c287]Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn:
You Only Live Once: Single-Life Reinforcement Learning. NeurIPS 2022 - [c286]Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov:
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. NeurIPS 2022 - [c285]Benjamin Eysenbach, Soumith Udatha, Russ Salakhutdinov, Sergey Levine:
Imitating Past Successes can be Very Suboptimal. NeurIPS 2022 - [c284]Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov:
Contrastive Learning as Goal-Conditioned Reinforcement Learning. NeurIPS 2022 - [c283]Han Qi, Yi Su, Aviral Kumar, Sergey Levine:
Data-Driven Offline Decision-Making via Invariant Representation Learning. NeurIPS 2022 - [c282]Siddharth Reddy, Sergey Levine, Anca D. Dragan:
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization. NeurIPS 2022 - [c281]Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS 2022 - [c280]Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar:
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. NeurIPS 2022 - [c279]Marvin Zhang, Sergey Levine, Chelsea Finn:
MEMO: Test Time Robustness via Adaptation and Augmentation. NeurIPS 2022 - [c278]Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, Sergey Levine:
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets. Robotics: Science and Systems 2022 - [c277]Dhruv Shah, Sergey Levine:
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints. Robotics: Science and Systems 2022 - [i376]Jathushan Rajasegaran, Chelsea Finn, Sergey Levine:
Fully Online Meta-Learning Without Task Boundaries. CoRR abs/2202.00263 (2022) - [i375]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. CoRR abs/2202.01741 (2022) - [i374]Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn:
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning. CoRR abs/2202.02005 (2022) - [i373]Sean Chen, Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine:
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning. CoRR abs/2202.02465 (2022) - [i372]Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine:
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. CoRR abs/2202.08450 (2022) - [i371]Dhruv Shah, Sergey Levine:
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints. CoRR abs/2202.11271 (2022) - [i370]Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca D. Dragan, Sergey Levine:
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback. CoRR abs/2203.02072 (2022) - [i369]Abhishek Gupta, Corey Lynch, Brandon Kinman, Garrett Peake, Sergey Levine, Karol Hausman:
Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning. CoRR abs/2203.15755 (2022) - [i368]Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan:
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. CoRR abs/2204.01691 (2022) - [i367]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. CoRR abs/2204.02372 (2022) - [i366]Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine:
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? CoRR abs/2204.05618 (2022) - [i365]Siddharth Verma, Justin Fu, Mengjiao Yang, Sergey Levine:
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning. CoRR abs/2204.08426 (2022) - [i364]Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine:
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL. CoRR abs/2204.08585 (2022) - [i363]Charlie Snell, Mengjiao Yang, Justin Fu, Yi Su, Sergey Levine:
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. CoRR abs/2204.10198 (2022) - [i362]Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine:
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. CoRR abs/2204.13060 (2022) - [i361]Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon:
Control-Aware Prediction Objectives for Autonomous Driving. CoRR abs/2204.13319 (2022) - [i360]Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, Sanja Fidler:
ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters. CoRR abs/2205.01906 (2022) - [i359]Kuan Fang, Patrick Yin, Ashvin Nair, Sergey Levine:
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space. CoRR abs/2205.08129 (2022) - [i358]Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine:
Planning with Diffusion for Flexible Behavior Synthesis. CoRR abs/2205.09991 (2022) - [i357]Siddharth Reddy, Sergey Levine, Anca D. Dragan:
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization. CoRR abs/2205.12381 (2022) - [i356]Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurams, Sergey Levine, Pieter Abbeel:
Multimodal Masked Autoencoders Learn Transferable Representations. CoRR abs/2205.14204 (2022) - [i355]Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR abs/2206.01367 (2022) - [i354]Benjamin Eysenbach, Soumith Udatha, Sergey Levine, Ruslan Salakhutdinov:
Imitating Past Successes can be Very Suboptimal. CoRR abs/2206.03378 (2022) - [i353]Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, Sergey Levine:
Contrastive Learning as Goal-Conditioned Reinforcement Learning. CoRR abs/2206.07568 (2022) - [i352]Katie Kang, Paula Gradu, Jason J. Choi, Michael Janner, Claire J. Tomlin, Sergey Levine:
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control. CoRR abs/2206.10524 (2022) - [i351]Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine:
Offline RL for Natural Language Generation with Implicit Language Q Learning. CoRR abs/2206.11871 (2022) - [i350]Michael Chang, Thomas L. Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR abs/2207.00787 (2022) - [i349]Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine:
Offline RL Policies Should be Trained to be Adaptive. CoRR abs/2207.02200 (2022) - [i348]Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine:
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action. CoRR abs/2207.04429 (2022) - [i347]Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine:
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. CoRR abs/2207.04703 (2022) - [i346]Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter:
Inner Monologue: Embodied Reasoning through Planning with Language Models. CoRR abs/2207.05608 (2022) - [i345]Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath:
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot. CoRR abs/2208.01160 (2022) - [i344]Marwa Abdulhai, Natasha Jaques, Sergey Levine:
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience. CoRR abs/2208.04919 (2022) - [i343]Laura M. Smith, Ilya Kostrikov, Sergey Levine:
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning. CoRR abs/2208.07860 (2022) - [i342]Gilbert Feng, Hongbo Zhang, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, Sergey Levine:
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots. CoRR abs/2209.05309 (2022) - [i341]Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov:
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. CoRR abs/2209.08466 (2022) - [i340]Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal:
Distributionally Adaptive Meta Reinforcement Learning. CoRR abs/2210.03104 (2022) - [i339]Dhruv Shah, Ajay Sridhar, Arjun Bhorkar, Noriaki Hirose, Sergey Levine:
GNM: A General Navigation Model to Drive Any Robot. CoRR abs/2210.03370 (2022) - [i338]Aviral Kumar, Anikait Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, Sergey Levine:
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials. CoRR abs/2210.05178 (2022) - [i337]Kuan Fang, Patrick Yin, Ashvin Nair, Homer Walke, Gengchen Yan, Sergey Levine:
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks. CoRR abs/2210.06601 (2022) - [i336]Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine:
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation. CoRR abs/2210.07450 (2022) - [i335]Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn:
You Only Live Once: Single-Life Reinforcement Learning. CoRR abs/2210.08863 (2022) - [i334]Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine:
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. CoRR abs/2210.09579 (2022) - [i333]Hao Liu, Xinyang Geng, Lisa Lee, Igor Mordatch, Sergey Levine, Sharan Narang, Pieter Abbeel:
FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners. CoRR abs/2210.13432 (2022) - [i332]Ashvin Nair, Brian Zhu, Gokul Narayanan, Eugen Solowjow, Sergey Levine:
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision. CoRR abs/2210.15206 (2022) - [i331]Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Professional-Level Go AIs. CoRR abs/2211.00241 (2022) - [i330]Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, Sergey Levine:
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints. CoRR abs/2211.01052 (2022) - [i329]Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar:
Dual Generator Offline Reinforcement Learning. CoRR abs/2211.01471 (2022) - [i328]Han Qi, Yi Su, Aviral Kumar, Sergey Levine:
Data-Driven Offline Decision-Making via Invariant Representation Learning. CoRR abs/2211.11349 (2022) - [i327]Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid, Anthony Brohan, Karol Hausman, Sergey Levine, Jonathan Tompson:
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models. CoRR abs/2211.11736 (2022) - [i326]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR abs/2211.15144 (2022) - [i325]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. CoRR abs/2212.00186 (2022) - [i324]Joey Hong, Aviral Kumar, Sergey Levine:
Confidence-Conditioned Value Functions for Offline Reinforcement Learning. CoRR abs/2212.04607 (2022) - [i323]Sergey Levine, Dhruv Shah:
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results. CoRR abs/2212.06759 (2022) - [i322]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-1: Robotics Transformer for Real-World Control at Scale. CoRR abs/2212.06817 (2022) - [i321]Dhruv Shah, Arjun Bhorkar, Hrish Leen, Ilya Kostrikov, Nick Rhinehart, Sergey Levine:
Offline Reinforcement Learning for Visual Navigation. CoRR abs/2212.08244 (2022) - [i320]Kelvin Xu, Zheyuan Hu, Ria Doshi, Aaron Rovinsky, Vikash Kumar, Abhishek Gupta, Sergey Levine:
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance. CoRR abs/2212.09902 (2022) - [i319]Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Becca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine:
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. CoRR abs/2212.11419 (2022) - [i318]Qiyang Li, Yuexiang Zhai, Yi Ma, Sergey Levine:
Understanding the Complexity Gains of Single-Task RL with a Curriculum. CoRR abs/2212.12809 (2022) - 2021
- [j19]Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, Sergey Levine:
How to train your robot with deep reinforcement learning: lessons we have learned. Int. J. Robotics Res. 40(4-5) (2021) - [j18]Gregory Kahn, Pieter Abbeel, Sergey Levine:
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics Autom. Lett. 6(2): 1312-1319 (2021) - [j17]Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine:
Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads. IEEE Robotics Autom. Lett. 6(2): 1471-1478 (2021) - [j16]Gregory Kahn, Pieter Abbeel, Sergey Levine:
LaND: Learning to Navigate From Disengagements. IEEE Robotics Autom. Lett. 6(2): 1872-1879 (2021) - [j15]Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa:
AMP: adversarial motion priors for stylized physics-based character control. ACM Trans. Graph. 40(4): 144:1-144:20 (2021) - [c276]Charles Sun, Jedrzej Orbik, Coline Manon Devin, Brian H. Yang, Abhishek Gupta, Glen Berseth, Sergey Levine:
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation. CoRL 2021: 308-319 - [c275]Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine:
A Workflow for Offline Model-Free Robotic Reinforcement Learning. CoRL 2021: 417-428 - [c274]Dmitry Kalashnikov, Jake Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman:
Scaling Up Multi-Task Robotic Reinforcement Learning. CoRL 2021: 557-575 - [c273]Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine:
Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRL 2021: 674-684 - [c272]Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn:
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning. CoRL 2021: 991-1002 - [c271]Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, Sergey Levine:
AW-Opt: Learning Robotic Skills with Imitation andReinforcement at Scale. CoRL 2021: 1078-1088 - [c270]Katie Kang, Gregory Kahn, Sergey Levine:
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots. CoRL 2021: 1316-1325 - [c269]Sergey Levine:
Understanding the World Through Action. CoRL 2021: 1752-1757 - [c268]Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR 2021 - [c267]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. ICLR 2021 - [c266]Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. ICLR 2021 - [c265]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. ICLR 2021 - [c264]John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V. Le, Sergey Levine, Honglak Lee, Aleksandra Faust:
Evolving Reinforcement Learning Algorithms. ICLR 2021 - [c263]Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov:
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. ICLR 2021 - [c262]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
C-Learning: Learning to Achieve Goals via Recursive Classification. ICLR 2021 - [c261]Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine:
Benchmarks for Deep Off-Policy Evaluation. ICLR 2021 - [c260]Justin Fu, Sergey Levine:
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation. ICLR 2021 - [c259]Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca D. Dragan, Sergey Levine:
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback. ICLR 2021 - [c258]Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine:
Learning to Reach Goals via Iterated Supervised Learning. ICLR 2021 - [c257]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer:
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments. ICLR 2021 - [c256]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. ICLR 2021 - [c255]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. ICLR 2021 - [c254]Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine:
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. ICLR 2021 - [c253]Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine:
Model-Based Visual Planning with Self-Supervised Functional Distances. ICLR 2021 - [c252]Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. ICML 2021: 1452-1462 - [c251]Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine:
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. ICML 2021: 1518-1528 - [c250]Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu:
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning. ICML 2021: 1953-1963 - [c249]Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. ICML 2021: 3305-3317 - [c248]Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu:
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. ICML 2021: 3541-3552 - [c247]Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran S. Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang:
WILDS: A Benchmark of in-the-Wild Distribution Shifts. ICML 2021: 5637-5664 - [c246]Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine:
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. ICML 2021: 6346-6356 - [c245]Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn:
Offline Meta-Reinforcement Learning with Advantage Weighting. ICML 2021: 7780-7791 - [c244]Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques:
Emergent Social Learning via Multi-agent Reinforcement Learning. ICML 2021: 7991-8004 - [c243]Oleh Rybkin, Kostas Daniilidis, Sergey Levine:
Simple and Effective VAE Training with Calibrated Decoders. ICML 2021: 9179-9189 - [c242]Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine:
Model-Based Reinforcement Learning via Latent-Space Collocation. ICML 2021: 9190-9201 - [c241]Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine:
Conservative Objective Models for Effective Offline Model-Based Optimization. ICML 2021: 10358-10368 - [c240]Aurick Zhou, Sergey Levine:
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. ICML 2021: 12803-12812 - [c239]Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots. ICRA 2021: 2811-2817 - [c238]Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, Sergey Levine, Jie Tan:
SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning. ICRA 2021: 2884-2890 - [c237]Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine:
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies. ICRA 2021: 6635-6641 - [c236]Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine:
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention. ICRA 2021: 6664-6671 - [c235]Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine:
ViNG: Learning Open-World Navigation with Visual Goals. ICRA 2021: 13215-13222 - [c234]Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine:
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models. ICRA 2021: 13663-13669 - [c233]Alexander Khazatsky, Ashvin Nair, Daniel Jing, Sergey Levine:
What Can I Do Here? Learning New Skills by Imagining Visual Affordances. ICRA 2021: 14291-14297 - [c232]Aurick Zhou, Sergey Levine:
Bayesian Adaptation for Covariate Shift. NeurIPS 2021: 914-927 - [c231]Michael Janner, Qiyang Li, Sergey Levine:
Offline Reinforcement Learning as One Big Sequence Modeling Problem. NeurIPS 2021: 1273-1286 - [c230]Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine:
Information is Power: Intrinsic Control via Information Capture. NeurIPS 2021: 10745-10758 - [c229]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. NeurIPS 2021: 11501-11516 - [c228]Ben Eysenbach, Sergey Levine, Ruslan Salakhutdinov:
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification. NeurIPS 2021: 11541-11552 - [c227]Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS 2021: 13045-13058 - [c226]Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Autonomous Reinforcement Learning via Subgoal Curricula. NeurIPS 2021: 18474-18486 - [c225]Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn:
Adaptive Risk Minimization: Learning to Adapt to Domain Shift. NeurIPS 2021: 23664-23678 - [c224]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS 2021: 25502-25515 - [c223]Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine:
Which Mutual-Information Representation Learning Objectives are Sufficient for Control? NeurIPS 2021: 26345-26357 - [c222]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Pragmatic Image Compression for Human-in-the-Loop Decision-Making. NeurIPS 2021: 26499-26510 - [c221]Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
Robust Predictable Control. NeurIPS 2021: 27813-27825 - [c220]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS 2021: 28954-28967 - [i317]John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust:
Evolving Reinforcement Learning Algorithms. CoRR abs/2101.03958 (2021) - [i316]Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, Sergey Levine, Jie Tan:
SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning. CoRR abs/2101.06005 (2021) - [i315]Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, Sergey Levine:
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned. CoRR abs/2102.02915 (2021) - [i314]Justin Fu, Sergey Levine:
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation. CoRR abs/2102.07970 (2021) - [i313]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR abs/2102.08363 (2021) - [i312]Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR abs/2102.12560 (2021) - [i311]Benjamin Eysenbach, Sergey Levine:
Maximum Entropy RL (Provably) Solves Some Robust RL Problems. CoRR abs/2103.06257 (2021) - [i310]Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov:
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification. CoRR abs/2103.12656 (2021) - [i309]Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu:
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. CoRR abs/2103.12726 (2021) - [i308]Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots. CoRR abs/2103.14295 (2021) - [i307]Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine:
Benchmarks for Deep Off-Policy Evaluation. CoRR abs/2103.16596 (2021) - [i306]Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa:
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control. CoRR abs/2104.02180 (2021) - [i305]Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine:
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRR abs/2104.05859 (2021) - [i304]Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine:
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. CoRR abs/2104.07749 (2021) - [i303]Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman:
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale. CoRR abs/2104.08212 (2021) - [i302]Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. CoRR abs/2104.10190 (2021) - [i301]Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine:
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models. CoRR abs/2104.10558 (2021) - [i300]Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine:
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention. CoRR abs/2104.11203 (2021) - [i299]Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine:
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies. CoRR abs/2104.11707 (2021) - [i298]Alexander Khazatsky, Ashvin Nair, Daniel Jing, Sergey Levine:
What Can I Do Here? Learning New Skills by Imagining Visual Affordances. CoRR abs/2106.00671 (2021) - [i297]Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu:
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning. CoRR abs/2106.01404 (2021) - [i296]Michael Janner, Qiyang Li, Sergey Levine:
Reinforcement Learning as One Big Sequence Modeling Problem. CoRR abs/2106.02039 (2021) - [i295]Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine:
Which Mutual-Information Representation Learning Objectives are Sufficient for Control? CoRR abs/2106.07278 (2021) - [i294]Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan:
FitVid: Overfitting in Pixel-Level Video Prediction. CoRR abs/2106.13195 (2021) - [i293]Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine:
Model-Based Reinforcement Learning via Latent-Space Collocation. CoRR abs/2106.13229 (2021) - [i292]Katie Kang, Gregory Kahn, Sergey Levine:
Multi-Robot Deep Reinforcement Learning for Mobile Navigation. CoRR abs/2106.13280 (2021) - [i291]Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR abs/2106.14993 (2021) - [i290]Vitchyr H. Pong, Ashvin Nair, Laura M. Smith, Catherine Huang, Sergey Levine:
Offline Meta-Reinforcement Learning with Online Self-Supervision. CoRR abs/2107.03974 (2021) - [i289]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR abs/2107.06277 (2021) - [i288]Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine:
Conservative Objective Models for Effective Offline Model-Based Optimization. CoRR abs/2107.06882 (2021) - [i287]Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine:
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. CoRR abs/2107.07184 (2021) - [i286]Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine:
Explore and Control with Adversarial Surprise. CoRR abs/2107.07394 (2021) - [i285]Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Persistent Reinforcement Learning via Subgoal Curricula. CoRR abs/2107.12931 (2021) - [i284]Charles Sun, Jedrzej Orbik, Coline Devin, Brian H. Yang, Abhishek Gupta, Glen Berseth, Sergey Levine:
ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors. CoRR abs/2107.13545 (2021) - [i283]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Pragmatic Image Compression for Human-in-the-Loop Decision-Making. CoRR abs/2108.04219 (2021) - [i282]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
Robust Predictable Control. CoRR abs/2109.03214 (2021) - [i281]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. CoRR abs/2109.08128 (2021) - [i280]Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine:
A Workflow for Offline Model-Free Robotic Reinforcement Learning. CoRR abs/2109.10813 (2021) - [i279]Aurick Zhou, Sergey Levine:
Training on Test Data with Bayesian Adaptation for Covariate Shift. CoRR abs/2109.12746 (2021) - [i278]Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, Sergey Levine:
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets. CoRR abs/2109.13396 (2021) - [i277]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
The Information Geometry of Unsupervised Reinforcement Learning. CoRR abs/2110.02719 (2021) - [i276]Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov:
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. CoRR abs/2110.02758 (2021) - [i275]Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jonathan Scholz, Stefan Schaal, Sergey Levine:
Offline Meta-Reinforcement Learning for Industrial Insertion. CoRR abs/2110.04276 (2021) - [i274]Laura M. Smith, J. Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine:
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World. CoRR abs/2110.05457 (2021) - [i273]Ilya Kostrikov, Ashvin Nair, Sergey Levine:
Offline Reinforcement Learning with Implicit Q-Learning. CoRR abs/2110.06169 (2021) - [i272]Marvin Zhang, Sergey Levine, Chelsea Finn:
MEMO: Test Time Robustness via Adaptation and Augmentation. CoRR abs/2110.09506 (2021) - [i271]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR abs/2110.11346 (2021) - [i270]Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez:
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks. CoRR abs/2110.12080 (2021) - [i269]Sergey Levine:
Understanding the World Through Action. CoRR abs/2110.12543 (2021) - [i268]Mengjiao Yang, Sergey Levine, Ofir Nachum:
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data. CoRR abs/2110.14770 (2021) - [i267]Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter:
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning. CoRR abs/2111.03189 (2021) - [i266]Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, Sergey Levine:
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale. CoRR abs/2111.05424 (2021) - [i265]Nitish Dashora, Daniel Shin, Dhruv Shah, Henry A. Leopold, David D. Fan, Ali-Akbar Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine:
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments. CoRR abs/2111.10948 (2021) - [i264]Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine:
Information is Power: Intrinsic Control via Information Capture. CoRR abs/2112.03899 (2021) - [i263]Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine:
CoMPS: Continual Meta Policy Search. CoRR abs/2112.04467 (2021) - [i262]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR abs/2112.04716 (2021) - [i261]Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang:
Extending the WILDS Benchmark for Unsupervised Adaptation. CoRR abs/2112.05090 (2021) - [i260]Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn:
Autonomous Reinforcement Learning: Formalism and Benchmarking. CoRR abs/2112.09605 (2021) - [i259]Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine:
RvS: What is Essential for Offline RL via Supervised Learning? CoRR abs/2112.10751 (2021) - 2020
- [j14]Saurabh Gupta, Varun Tolani, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik:
Cognitive Mapping and Planning for Visual Navigation. Int. J. Comput. Vis. 128(5): 1311-1330 (2020) - [j13]Brian H. Yang, Dinesh Jayaraman, Glen Berseth, Alexei A. Efros, Sergey Levine:
Morphology-Agnostic Visual Robotic Control. IEEE Robotics Autom. Lett. 5(2): 766-773 (2020) - [j12]Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg:
Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks. IEEE Robotics Autom. Lett. 5(2): 3612-3619 (2020) - [c219]Sergey Levine:
Unsupervised Reinforcement Learning. AAMAS 2020: 5-6 - [c218]Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nicholas Rhinehart:
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting. CoRL 2020: 11-20 - [c217]Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn:
Reinforcement Learning with Videos: Combining Offline Observations with Interaction. CoRL 2020: 339-354 - [c216]Siddharth Reddy, Sergey Levine, Anca D. Dragan:
Assisted Perception: Optimizing Observations to Communicate State. CoRL 2020: 748-764 - [c215]Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan:
Learning to Walk in the Real World with Minimal Human Effort. CoRL 2020: 1110-1120 - [c214]Zihao Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine:
MELD: Meta-Reinforcement Learning from Images via Latent State Models. CoRL 2020: 1246-1261 - [c213]Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman:
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning. CoRL 2020: 2120-2136 - [c212]Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine:
Chaining Behaviors from Data with Model-Free Reinforcement Learning. CoRL 2020: 2162-2177 - [c211]Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, Mohi Khansari:
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real. CVPR 2020: 11154-11163 - [c210]Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn:
Learning Predictive Models from Observation and Interaction. ECCV (20) 2020: 708-725 - [c209]Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. ICLR 2020 - [c208]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. ICLR 2020 - [c207]Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio:
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. ICLR 2020 - [c206]Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine:
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery. ICLR 2020 - [c205]Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski:
Model Based Reinforcement Learning for Atari. ICLR 2020 - [c204]Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma:
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. ICLR 2020 - [c203]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards. ICLR 2020 - [c202]Nicholas Rhinehart, Rowan McAllister, Sergey Levine:
Deep Imitative Models for Flexible Inference, Planning, and Control. ICLR 2020 - [c201]Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman:
Dynamics-Aware Unsupervised Discovery of Skills. ICLR 2020 - [c200]Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog:
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control. ICLR 2020 - [c199]Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn:
Meta-Learning without Memorization. ICLR 2020 - [c198]Allan Zhou, Eric Jang, Daniel Kappler, Alexander Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn:
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards. ICLR 2020 - [c197]Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine:
The Ingredients of Real World Robotic Reinforcement Learning. ICLR 2020 - [c196]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. ICML 2020: 1437-1447 - [c195]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020: 3145-3153 - [c194]Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine:
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning. ICML 2020: 7783-7792 - [c193]Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike:
Learning Human Objectives by Evaluating Hypothetical Behavior. ICML 2020: 8020-8029 - [c192]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. ICML 2020: 11055-11065 - [c191]Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar:
TRASS: Time Reversal as Self-Supervision. ICRA 2020: 115-121 - [c190]Akhil Padmanabha, Frederik Ebert, Stephen Tian, Roberto Calandra, Chelsea Finn, Sergey Levine:
OmniTact: A Multi-Directional High-Resolution Touch Sensor. ICRA 2020: 618-624 - [c189]Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn:
Scalable Multi-Task Imitation Learning with Autonomous Improvement. ICRA 2020: 2167-2173 - [c188]Gokul Swamy, Siddharth Reddy, Sergey Levine, Anca D. Dragan:
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets. ICRA 2020: 5942-5948 - [c187]Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine:
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards. IROS 2020: 5548-5555 - [c186]Gerrit Schoettler, Ashvin Nair, Juan Aparicio Ojea, Sergey Levine, Eugen Solowjow:
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks. IROS 2020: 9728-9735 - [c185]Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine:
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. NeurIPS 2020 - [c184]Ben Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov:
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. NeurIPS 2020 - [c183]Michael Janner, Igor Mordatch, Sergey Levine:
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction. NeurIPS 2020 - [c182]Aviral Kumar, Abhishek Gupta, Sergey Levine:
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. NeurIPS 2020 - [c181]Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn:
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. NeurIPS 2020 - [c180]Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. NeurIPS 2020 - [c179]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS 2020 - [c178]Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. NeurIPS 2020 - [c177]Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine:
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. NeurIPS 2020 - [c176]Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine:
Continual Learning of Control Primitives : Skill Discovery via Reset-Games. NeurIPS 2020 - [c175]Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. NeurIPS 2020 - [c174]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. NeurIPS 2020 - [c173]Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Edward Lee, Jie Tan, Sergey Levine:
Learning Agile Robotic Locomotion Skills by Imitating Animals. Robotics: Science and Systems 2020 - [c172]Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu:
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning. Robotics: Science and Systems 2020 - [c171]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. Robotics: Science and Systems 2020 - [i258]Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. CoRR abs/2001.06782 (2020) - [i257]Gregory Kahn, Pieter Abbeel, Sergey Levine:
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. CoRR abs/2002.05700 (2020) - [i256]Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan:
Learning to Walk in the Real World with Minimal Human Effort. CoRR abs/2002.08550 (2020) - [i255]Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov:
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. CoRR abs/2002.11089 (2020) - [i254]Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn:
Scalable Multi-Task Imitation Learning with Autonomous Improvement. CoRR abs/2003.02636 (2020) - [i253]Akhil Padmanabha, Frederik Ebert, Stephen Tian, Roberto Calandra, Chelsea Finn, Sergey Levine:
OmniTact: A Multi-Directional High Resolution Touch Sensor. CoRR abs/2003.06965 (2020) - [i252]Aviral Kumar, Abhishek Gupta, Sergey Levine:
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. CoRR abs/2003.07305 (2020) - [i251]Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nicholas Rhinehart:
Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting. CoRR abs/2003.08376 (2020) - [i250]Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Edward Lee, Jie Tan, Sergey Levine:
Learning Agile Robotic Locomotion Skills by Imitating Animals. CoRR abs/2004.00784 (2020) - [i249]Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog:
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control. CoRR abs/2004.06089 (2020) - [i248]Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine:
D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR abs/2004.07219 (2020) - [i247]Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman:
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation. CoRR abs/2004.10190 (2020) - [i246]Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine:
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads. CoRR abs/2004.11345 (2020) - [i245]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. CoRR abs/2004.11935 (2020) - [i244]Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine:
The Ingredients of Real-World Robotic Reinforcement Learning. CoRR abs/2004.12570 (2020) - [i243]Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu:
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning. CoRR abs/2004.12974 (2020) - [i242]Gerrit Schoettler, Ashvin Nair, Juan Aparicio Ojea, Sergey Levine, Eugen Solowjow:
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks. CoRR abs/2004.14404 (2020) - [i241]Sergey Levine, Aviral Kumar, George Tucker, Justin Fu:
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR abs/2005.01643 (2020) - [i240]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. CoRR abs/2005.13239 (2020) - [i239]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. CoRR abs/2006.04779 (2020) - [i238]Russell Mendonca, Xinyang Geng, Chelsea Finn, Sergey Levine:
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling. CoRR abs/2006.07178 (2020) - [i237]Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, Mohi Khansari:
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real. CoRR abs/2006.09001 (2020) - [i236]Ashvin Nair, Murtaza Dalal, Abhishek Gupta, Sergey Levine:
Accelerating Online Reinforcement Learning with Offline Datasets. CoRR abs/2006.09359 (2020) - [i235]Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR abs/2006.10742 (2020) - [i234]John D. Co-Reyes, Suvansh Sanjeev, Glen Berseth, Abhishek Gupta, Sergey Levine:
Ecological Reinforcement Learning. CoRR abs/2006.12478 (2020) - [i233]Oleh Rybkin, Kostas Daniilidis, Sergey Levine:
Simple and Effective VAE Training with Calibrated Decoders. CoRR abs/2006.13202 (2020) - [i232]Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. CoRR abs/2006.13205 (2020) - [i231]Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari, Ruslan Salakhutdinov, Sergey Levine:
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. CoRR abs/2006.13916 (2020) - [i230]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR abs/2006.14911 (2020) - [i229]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer:
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems. CoRR abs/2006.16225 (2020) - [i228]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR abs/2007.02382 (2020) - [i227]Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine, Chelsea Finn:
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. CoRR abs/2007.02931 (2020) - [i226]Siddharth Reddy, Sergey Levine, Anca D. Dragan:
Assisted Perception: Optimizing Observations to Communicate State. CoRR abs/2008.02840 (2020) - [i225]Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn:
Offline Meta-Reinforcement Learning with Advantage Weighting. CoRR abs/2008.06043 (2020) - [i224]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. CoRR abs/2008.06622 (2020) - [i223]Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques:
Multi-agent Social Reinforcement Learning Improves Generalization. CoRR abs/2010.00581 (2020) - [i222]Gregory Kahn, Pieter Abbeel, Sergey Levine:
LaND: Learning to Navigate from Disengagements. CoRR abs/2010.04689 (2020) - [i221]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. CoRR abs/2010.13611 (2020) - [i220]Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine:
MELD: Meta-Reinforcement Learning from Images via Latent State Models. CoRR abs/2010.13957 (2020) - [i219]Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn:
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. CoRR abs/2010.14484 (2020) - [i218]Michael Janner, Igor Mordatch, Sergey Levine:
γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction. CoRR abs/2010.14496 (2020) - [i217]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. CoRR abs/2010.14497 (2020) - [i216]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. CoRR abs/2010.14498 (2020) - [i215]Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine:
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. CoRR abs/2010.14500 (2020) - [i214]Dhruv Batra, Angel X. Chang, Sonia Chernova, Andrew J. Davison, Jia Deng, Vladlen Koltun, Sergey Levine, Jitendra Malik, Igor Mordatch, Roozbeh Mottaghi, Manolis Savva, Hao Su:
Rearrangement: A Challenge for Embodied AI. CoRR abs/2011.01975 (2020) - [i213]Aurick Zhou, Sergey Levine:
Amortized Conditional Normalized Maximum Likelihood. CoRR abs/2011.02696 (2020) - [i212]Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine:
Continual Learning of Control Primitives: Skill Discovery via Reset-Games. CoRR abs/2011.05286 (2020) - [i211]Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn:
Reinforcement Learning with Videos: Combining Offline Observations with Interaction. CoRR abs/2011.06507 (2020) - [i210]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
C-Learning: Learning to Achieve Goals via Recursive Classification. CoRR abs/2011.08909 (2020) - [i209]Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine:
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. CoRR abs/2011.10024 (2020) - [i208]Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine:
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. CoRR abs/2012.02096 (2020) - [i207]Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Harini Kannan, Chelsea Finn, Sergey Levine, Dumitru Erhan:
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning. CoRR abs/2012.04603 (2020) - [i206]Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang:
WILDS: A Benchmark of in-the-Wild Distribution Shifts. CoRR abs/2012.07421 (2020) - [i205]Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine:
Variable-Shot Adaptation for Online Meta-Learning. CoRR abs/2012.07769 (2020) - [i204]Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine:
ViNG: Learning Open-World Navigation with Visual Goals. CoRR abs/2012.09812 (2020) - [i203]Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine:
Model-Based Visual Planning with Self-Supervised Functional Distances. CoRR abs/2012.15373 (2020)
2010 – 2019
- 2019
- [j11]Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, Kristofer S. J. Pister:
Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning. IEEE Robotics Autom. Lett. 4(4): 4224-4230 (2019) - [c170]Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine:
Contextual Imagined Goals for Self-Supervised Robotic Learning. CoRL 2019: 530-539 - [c169]Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn:
RoboNet: Large-Scale Multi-Robot Learning. CoRL 2019: 885-897 - [c168]Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman:
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning. CoRL 2019: 1025-1037 - [c167]Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, Sergey Levine:
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. CoRL 2019: 1094-1100 - [c166]Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar:
Deep Dynamics Models for Learning Dexterous Manipulation. CoRL 2019: 1101-1112 - [c165]Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet:
Learning Latent Plans from Play. CoRL 2019: 1113-1132 - [c164]Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar:
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots. CoRL 2019: 1300-1313 - [c163]Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine:
Entity Abstraction in Visual Model-Based Reinforcement Learning. CoRL 2019: 1439-1456 - [c162]Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis:
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks. CVPR 2019: 12627-12637 - [c161]Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine:
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings. ICCV 2019: 2821-2830 - [c160]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) 2019 - [c159]John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine:
Guiding Policies with Language via Meta-Learning. ICLR (Poster) 2019 - [c158]Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine:
Diversity is All You Need: Learning Skills without a Reward Function. ICLR (Poster) 2019 - [c157]Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama:
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following. ICLR (Poster) 2019 - [c156]Dibya Ghosh, Abhishek Gupta, Sergey Levine:
Learning Actionable Representations with Goal Conditioned Policies. ICLR (Poster) 2019 - [c155]Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. ICLR (Poster) 2019 - [c154]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew M. Botvinick, Yoshua Bengio, Sergey Levine:
InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) 2019 - [c153]Kyle Hsu, Sergey Levine, Chelsea Finn:
Unsupervised Learning via Meta-Learning. ICLR (Poster) 2019 - [c152]Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu:
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning. ICLR (Poster) 2019 - [c151]Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine:
Time-Agnostic Prediction: Predicting Predictable Video Frames. ICLR (Poster) 2019 - [c150]Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson:
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning. ICLR (Poster) 2019 - [c149]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. ICLR (Poster) 2019 - [c148]Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. ICLR (Poster) 2019 - [c147]Anusha Nagabandi, Chelsea Finn, Sergey Levine:
Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. ICLR (Poster) 2019 - [c146]Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine:
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow. ICLR (Poster) 2019 - [c145]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. ICML 2019: 1920-1930 - [c144]Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. ICML 2019: 2021-2030 - [c143]Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song:
EMI: Exploration with Mutual Information. ICML 2019: 3360-3369 - [c142]Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen:
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. ICML 2019: 5331-5340 - [c141]Kelvin Xu, Ellis Ratner, Anca D. Dragan, Sergey Levine, Chelsea Finn:
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. ICML 2019: 6952-6962 - [c140]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. ICML 2019: 7444-7453 - [c139]Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine:
Manipulation by Feel: Touch-Based Control with Deep Predictive Models. ICRA 2019: 818-824 - [c138]Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine:
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty. ICRA 2019: 2083-2089 - [c137]Thomas Liao, Grant Wang, Brian H. Yang, Rene Lee, Kristofer S. J. Pister, Sergey Levine, Roberto Calandra:
Data-efficient Learning of Morphology and Controller for a Microrobot. ICRA 2019: 2488-2494 - [c136]Justin Lin, Roberto Calandra, Sergey Levine:
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching. ICRA 2019: 3644-3650 - [c135]Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar:
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. ICRA 2019: 3651-3657 - [c134]Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine:
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight. ICRA 2019: 6008-6014 - [c133]Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine:
Residual Reinforcement Learning for Robot Control. ICRA 2019: 6023-6029 - [c132]Brian H. Yang, Dinesh Jayaraman, Jesse Zhang, Sergey Levine:
REPLAB: A Reproducible Low-Cost Arm Benchmark for Robotic Learning. ICRA 2019: 8691-8697 - [c131]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Composition of Vision-Based Skills from Demonstration. IROS 2019: 2643-2650 - [c130]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. NeurIPS 2019: 113-124 - [c129]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. NeurIPS 2019: 3681-3692 - [c128]Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine:
Off-Policy Evaluation via Off-Policy Classification. NeurIPS 2019: 5438-5449 - [c127]Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Guided Meta-Policy Search. NeurIPS 2019: 9653-9664 - [c126]Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn:
Unsupervised Curricula for Visual Meta-Reinforcement Learning. NeurIPS 2019: 10519-10530 - [c125]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. NeurIPS 2019: 11693-11704 - [c124]Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. NeurIPS 2019: 11761-11771 - [c123]Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine:
When to Trust Your Model: Model-Based Policy Optimization. NeurIPS 2019: 12498-12509 - [c122]Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine:
Planning with Goal-Conditioned Policies. NeurIPS 2019: 14814-14825 - [c121]Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Compositional Plan Vectors. NeurIPS 2019: 14963-14974 - [c120]Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. NeurIPS 2019: 15220-15231 - [c119]Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aäron van den Oord, Sergey Levine, Pierre Sermanet:
Wasserstein Dependency Measure for Representation Learning. NeurIPS 2019: 15578-15588 - [c118]Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems 2019 - [c117]Avi Singh, Larry Yang, Chelsea Finn, Sergey Levine:
End-To-End Robotic Reinforcement Learning without Reward Engineering. Robotics: Science and Systems 2019 - [c116]Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn:
Improvisation through Physical Understanding: Using Novel Objects As Tools with Visual Foresight. Robotics: Science and Systems 2019 - [i202]Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine, Kristofer S. J. Pister:
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement learning. CoRR abs/1901.03737 (2019) - [i201]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew M. Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio:
InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR abs/1901.10902 (2019) - [i200]Lukasz Kidzinski, Carmichael F. Ong, Sharada Prasanna Mohanty, Jennifer L. Hicks, Sean F. Carroll, Bo Zhou, Hong-cheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaskowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy D. Watson, Marcel Salathé, Sergey Levine, Scott L. Delp:
Artificial Intelligence for Prosthetics - challenge solutions. CoRR abs/1902.02441 (2019) - [i199]Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine:
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight. CoRR abs/1902.03701 (2019) - [i198]Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama:
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following. CoRR abs/1902.07742 (2019) - [i197]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. CoRR abs/1902.08438 (2019) - [i196]Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. CoRR abs/1902.10250 (2019) - [i195]Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Ryan Sepassi, George Tucker, Henryk Michalewski:
Model-Based Reinforcement Learning for Atari. CoRR abs/1903.00374 (2019) - [i194]Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma:
VideoFlow: A Flow-Based Generative Model for Video. CoRR abs/1903.01434 (2019) - [i193]Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet:
Learning Latent Plans from Play. CoRR abs/1903.01973 (2019) - [i192]Justin Lin, Roberto Calandra, Sergey Levine:
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching. CoRR abs/1903.03591 (2019) - [i191]Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine:
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning. CoRR abs/1903.03698 (2019) - [i190]Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine:
Manipulation by Feel: Touch-Based Control with Deep Predictive Models. CoRR abs/1903.04128 (2019) - [i189]Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine:
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. CoRR abs/1903.08254 (2019) - [i188]Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aäron van den Oord, Sergey Levine, Pierre Sermanet:
Wasserstein Dependency Measure for Representation Learning. CoRR abs/1903.11780 (2019) - [i187]Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Guided Meta-Policy Search. CoRR abs/1904.00956 (2019) - [i186]Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn:
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight. CoRR abs/1904.05538 (2019) - [i185]Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine:
End-to-End Robotic Reinforcement Learning without Reward Engineering. CoRR abs/1904.07854 (2019) - [i184]Nicholas Rhinehart, Rowan McAllister, Kris M. Kitani, Sergey Levine:
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings. CoRR abs/1905.01296 (2019) - [i183]Thomas Liao, Grant Wang, Brian H. Yang, Rene Lee, Kristofer S. J. Pister, Sergey Levine, Roberto Calandra:
Data-efficient Learning of Morphology and Controller for a Microrobot. CoRR abs/1905.01334 (2019) - [i182]Brian H. Yang, Jesse Zhang, Vitchyr Pong, Sergey Levine, Dinesh Jayaraman:
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning. CoRR abs/1905.07447 (2019) - [i181]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. CoRR abs/1905.09808 (2019) - [i180]Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. CoRR abs/1905.10615 (2019) - [i179]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
SQIL: Imitation Learning via Regularized Behavioral Cloning. CoRR abs/1905.11108 (2019) - [i178]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. CoRR abs/1905.11979 (2019) - [i177]Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg:
Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations. CoRR abs/1905.13402 (2019) - [i176]Aviral Kumar, Justin Fu, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. CoRR abs/1906.00949 (2019) - [i175]Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine:
Off-Policy Evaluation via Off-Policy Classification. CoRR abs/1906.01624 (2019) - [i174]Allan Zhou, Eric Jang, Daniel Kappler, Alexander Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn:
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward. CoRR abs/1906.03352 (2019) - [i173]Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang:
Learning Powerful Policies by Using Consistent Dynamics Model. CoRR abs/1906.04355 (2019) - [i172]Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. CoRR abs/1906.05253 (2019) - [i171]Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, Ruslan Salakhutdinov:
Efficient Exploration via State Marginal Matching. CoRR abs/1906.05274 (2019) - [i170]Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine:
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards. CoRR abs/1906.05841 (2019) - [i169]Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine:
When to Trust Your Model: Model-Based Policy Optimization. CoRR abs/1906.08253 (2019) - [i168]Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio:
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. CoRR abs/1906.10667 (2019) - [i167]Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. CoRR abs/1907.00953 (2019) - [i166]Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman:
Dynamics-Aware Unsupervised Discovery of Skills. CoRR abs/1907.01657 (2019) - [i165]Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine:
Dynamical Distance Learning for Unsupervised and Semi-Supervised Skill Discovery. CoRR abs/1907.08225 (2019) - [i164]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. CoRR abs/1909.04630 (2019) - [i163]Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine:
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? CoRR abs/1909.10618 (2019) - [i162]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. CoRR abs/1909.10893 (2019) - [i161]Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar:
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots. CoRR abs/1909.11639 (2019) - [i160]Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar:
Deep Dynamics Models for Learning Dexterous Manipulation. CoRR abs/1909.11652 (2019) - [i159]Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine:
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning. CoRR abs/1910.00177 (2019) - [i158]Benjamin Eysenbach, Sergey Levine:
If MaxEnt RL is the Answer, What is the Question? CoRR abs/1910.01913 (2019) - [i157]Gokul Swamy, Siddharth Reddy, Sergey Levine, Anca D. Dragan:
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets. CoRR abs/1910.02910 (2019) - [i156]Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, Sergey Levine:
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. CoRR abs/1910.10897 (2019) - [i155]Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn:
RoboNet: Large-Scale Multi-Robot Learning. CoRR abs/1910.11215 (2019) - [i154]Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine:
Contextual Imagined Goals for Self-Supervised Robotic Learning. CoRR abs/1910.11670 (2019) - [i153]Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman:
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning. CoRR abs/1910.11956 (2019) - [i152]Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine:
Entity Abstraction in Visual Model-Based Reinforcement Learning. CoRR abs/1910.12827 (2019) - [i151]Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control. CoRR abs/1910.14033 (2019) - [i150]Soroush Nasiriany, Vitchyr H. Pong, Steven Lin, Sergey Levine:
Planning with Goal-Conditioned Policies. CoRR abs/1911.08453 (2019) - [i149]Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn:
Meta-Learning without Memorization. CoRR abs/1912.03820 (2019) - [i148]Allan Jabri, Kyle Hsu, Ben Eysenbach, Abhishek Gupta, Sergey Levine, Chelsea Finn:
Unsupervised Curricula for Visual Meta-Reinforcement Learning. CoRR abs/1912.04226 (2019) - [i147]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. CoRR abs/1912.04443 (2019) - [i146]Glen Berseth, Daniel Geng, Coline Devin, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
SMiRL: Surprise Minimizing RL in Dynamic Environments. CoRR abs/1912.05510 (2019) - [i145]Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike:
Learning Human Objectives by Evaluating Hypothetical Behavior. CoRR abs/1912.05652 (2019) - [i144]Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine:
Learning To Reach Goals Without Reinforcement Learning. CoRR abs/1912.06088 (2019) - [i143]Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn:
Learning Predictive Models From Observation and Interaction. CoRR abs/1912.12773 (2019) - [i142]Brian H. Yang, Dinesh Jayaraman, Glen Berseth, Alexei A. Efros, Sergey Levine:
Morphology-Agnostic Visual Robotic Control. CoRR abs/1912.13360 (2019) - [i141]Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. CoRR abs/1912.13464 (2019) - [i140]Aviral Kumar, Xue Bin Peng, Sergey Levine:
Reward-Conditioned Policies. CoRR abs/1912.13465 (2019) - 2018
- [j10]Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, Deirdre Quillen:
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robotics Res. 37(4-5): 421-436 (2018) - [j9]Brian H. Yang, Grant Wang, Roberto Calandra, Daniel Contreras, Sergey Levine, Kristofer S. J. Pister:
Learning Flexible and Reusable Locomotion Primitives for a Microrobot. IEEE Robotics Autom. Lett. 3(3): 1904-1911 (2018) - [j8]Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine:
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch. IEEE Robotics Autom. Lett. 3(4): 3300-3307 (2018) - [j7]Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel van de Panne:
DeepMimic: example-guided deep reinforcement learning of physics-based character skills. ACM Trans. Graph. 37(4): 143 (2018) - [j6]Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine:
SFV: reinforcement learning of physical skills from videos. ACM Trans. Graph. 37(6): 178 (2018) - [c115]Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn:
Few-Shot Goal Inference for Visuomotor Learning and Planning. CoRL 2018: 40-52 - [c114]Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine:
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping. CoRL 2018: 99-112 - [c113]Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine:
Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. CoRL 2018: 651-673 - [c112]Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine:
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation. CoRL 2018: 806-816 - [c111]Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn:
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning. CoRL 2018: 983-993 - [c110]Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik:
Learning Instance Segmentation by Interaction. CVPR Workshops 2018: 2042-2045 - [c109]Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine:
Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control. CVPR 2018: 4691-4699 - [c108]Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine:
Stochastic Variational Video Prediction. ICLR (Poster) 2018 - [c107]Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine:
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. ICLR (Poster) 2018 - [c106]Chelsea Finn, Sergey Levine:
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm. ICLR (Poster) 2018 - [c105]Justin Fu, Katie Luo, Sergey Levine:
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning. ICLR (Poster) 2018 - [c104]Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell:
Reinforcement Learning from Imperfect Demonstrations. ICLR (Workshop) 2018 - [c103]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. ICLR (Poster) 2018 - [c102]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) 2018 - [c101]Peter H. Jin, Sergey Levine, Kurt Keutzer:
Regret Minimization for Partially Observable Deep Reinforcement Learning. ICLR (Workshop) 2018 - [c100]Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine:
Temporal Difference Models: Model-Free Deep RL for Model-Based Control. ICLR (Poster) 2018 - [c99]Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alyosha A. Efros, Sergey Levine:
Conditional Networks for Few-Shot Semantic Segmentation. ICLR (Workshop) 2018 - [c98]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) 2018 - [c97]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. ICLR (Workshop) 2018 - [c96]John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine:
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. ICML 2018: 1008-1017 - [c95]Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine:
Latent Space Policies for Hierarchical Reinforcement Learning. ICML 2018: 1846-1855 - [c94]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. ICML 2018: 1856-1865 - [c93]Peter H. Jin, Kurt Keutzer, Sergey Levine:
Regret Minimization for Partially Observable Deep Reinforcement Learning. ICML 2018: 2347-2356 - [c92]Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control. ICML 2018: 4739-4748 - [c91]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML 2018: 5022-5031 - [c90]Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine:
Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. ICRA 2018: 1-8 - [c89]Yuxuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation. ICRA 2018: 1118-1125 - [c88]Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine:
Time-Contrastive Networks: Self-Supervised Learning from Video. ICRA 2018: 1134-1141 - [c87]Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine:
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration. ICRA 2018: 3758-3765 - [c86]Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke:
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping. ICRA 2018: 4243-4250 - [c85]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. ICRA 2018: 6244-6251 - [c84]Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine:
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. ICRA 2018: 6284-6291 - [c83]Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Deep Object-Centric Representations for Generalizable Robot Learning. ICRA 2018: 7111-7118 - [c82]Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine:
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning. ICRA 2018: 7559-7566 - [c81]Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. Fearing:
Learning Image-Conditioned Dynamics Models for Control of Underactuated Legged Millirobots. IROS 2018: 4606-4613 - [c80]Jacob Andreas, Dan Klein, Sergey Levine:
Learning with Latent Language. NAACL-HLT 2018: 2166-2179 - [c79]Ashish Kumar, Saurabh Gupta, David F. Fouhey, Sergey Levine, Jitendra Malik:
Visual Memory for Robust Path Following. NeurIPS 2018: 773-782 - [c78]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior. NeurIPS 2018: 1461-1472 - [c77]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Data-Efficient Hierarchical Reinforcement Learning. NeurIPS 2018: 3307-3317 - [c76]Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine:
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. NeurIPS 2018: 4759-4770 - [c75]Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Meta-Reinforcement Learning of Structured Exploration Strategies. NeurIPS 2018: 5307-5316 - [c74]Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine:
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. NeurIPS 2018: 8547-8556 - [c73]Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine:
Visual Reinforcement Learning with Imagined Goals. NeurIPS 2018: 9209-9220 - [c72]Chelsea Finn, Kelvin Xu, Sergey Levine:
Probabilistic Model-Agnostic Meta-Learning. NeurIPS 2018: 9537-9548 - [c71]Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine:
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. Robotics: Science and Systems 2018 - [c70]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Shared Autonomy via Deep Reinforcement Learning. Robotics: Science and Systems 2018 - [c69]Tianhe Yu, Chelsea Finn, Sudeep Dasari, Annie Xie, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. Robotics: Science and Systems 2018 - [i139]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. CoRR abs/1801.01290 (2018) - [i138]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR abs/1801.08930 (2018) - [i137]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. CoRR abs/1802.01557 (2018) - [i136]Siddharth Reddy, Sergey Levine, Anca D. Dragan:
Shared Autonomy via Deep Reinforcement Learning. CoRR abs/1802.01744 (2018) - [i135]Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell:
Reinforcement Learning from Imperfect Demonstrations. CoRR abs/1802.05313 (2018) - [i134]Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine:
Diversity is All You Need: Learning Skills without a Reward Function. CoRR abs/1802.06070 (2018) - [i133]Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Meta-Reinforcement Learning of Structured Exploration Strategies. CoRR abs/1802.07245 (2018) - [i132]Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine:
Temporal Difference Models: Model-Free Deep RL for Model-Based Control. CoRR abs/1802.09081 (2018) - [i131]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR abs/1802.10031 (2018) - [i130]Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine:
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. CoRR abs/1802.10264 (2018) - [i129]Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine:
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. CoRR abs/1803.00101 (2018) - [i128]Brian H. Yang, Grant Wang, Roberto Calandra, Daniel Contreras, Sergey Levine, Kristofer S. J. Pister:
Learning Flexible and Reusable Locomotion Primitives for a Microrobot. CoRR abs/1803.00196 (2018) - [i127]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. CoRR abs/1803.06773 (2018) - [i126]Ignasi Clavera, Anusha Nagabandi, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Learning to Adapt: Meta-Learning for Model-Based Control. CoRR abs/1803.11347 (2018) - [i125]Lukasz Kidzinski, Sharada P. Mohanty, Carmichael F. Ong, Jennifer L. Hicks, Sean F. Carroll, Sergey Levine, Marcel Salathé, Scott L. Delp:
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning. CoRR abs/1804.00198 (2018) - [i124]Lukasz Kidzinski, Sharada Prasanna Mohanty, Carmichael F. Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey M. Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Milos, Blazej Osinski, Andrew Melnik, Malte Schilling, Helge J. Ritter, Sean F. Carroll, Jennifer L. Hicks, Sergey Levine, Marcel Salathé, Scott L. Delp:
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments. CoRR abs/1804.00361 (2018) - [i123]Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR abs/1804.00379 (2018) - [i122]Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Universal Planning Networks. CoRR abs/1804.00645 (2018) - [i121]Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine:
Stochastic Adversarial Video Prediction. CoRR abs/1804.01523 (2018) - [i120]Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel van de Panne:
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills. CoRR abs/1804.02717 (2018) - [i119]Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine:
Latent Space Policies for Hierarchical Reinforcement Learning. CoRR abs/1804.02808 (2018) - [i118]Sergey Levine:
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review. CoRR abs/1805.00909 (2018) - [i117]Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior. CoRR abs/1805.08010 (2018) - [i116]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Data-Efficient Hierarchical Reinforcement Learning. CoRR abs/1805.08296 (2018) - [i115]Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine:
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch. CoRR abs/1805.11085 (2018) - [i114]Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine:
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. CoRR abs/1805.11686 (2018) - [i113]Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine:
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. CoRR abs/1805.12114 (2018) - [i112]Kelvin Xu, Ellis Ratner, Anca D. Dragan, Sergey Levine, Chelsea Finn:
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. CoRR abs/1805.12573 (2018) - [i111]John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine:
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. CoRR abs/1806.02813 (2018) - [i110]Chelsea Finn, Kelvin Xu, Sergey Levine:
Probabilistic Model-Agnostic Meta-Learning. CoRR abs/1806.02817 (2018) - [i109]Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine:
Unsupervised Meta-Learning for Reinforcement Learning. CoRR abs/1806.04640 (2018) - [i108]Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine:
Few-Shot Segmentation Propagation with Guided Networks. CoRR abs/1806.07373 (2018) - [i107]Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik:
Learning Instance Segmentation by Interaction. CoRR abs/1806.08354 (2018) - [i106]Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine:
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. CoRR abs/1806.10293 (2018) - [i105]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. CoRR abs/1807.04640 (2018) - [i104]Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine:
Visual Reinforcement Learning with Imagined Goals. CoRR abs/1807.04742 (2018) - [i103]Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine:
Time-Agnostic Prediction: Predicting Predictable Video Frames. CoRR abs/1808.07784 (2018) - [i102]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. CoRR abs/1808.09105 (2018) - [i101]Ilya Kostrikov, Kumar Krishna Agrawal, Sergey Levine, Jonathan Tompson:
Addressing Sample Inefficiency and Reward Bias in Inverse Reinforcement Learning. CoRR abs/1809.02925 (2018) - [i100]Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn:
Few-Shot Goal Inference for Visuomotor Learning and Planning. CoRR abs/1810.00482 (2018) - [i99]Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine:
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow. CoRR abs/1810.00821 (2018) - [i98]Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar:
Time Reversal as Self-Supervision. CoRR abs/1810.01128 (2018) - [i97]Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song:
EMI: Exploration with Mutual Information Maximizing State and Action Embeddings. CoRR abs/1810.01176 (2018) - [i96]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. CoRR abs/1810.01257 (2018) - [i95]Kyle Hsu, Sergey Levine, Chelsea Finn:
Unsupervised Learning via Meta-Learning. CoRR abs/1810.02334 (2018) - [i94]Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn:
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning. CoRR abs/1810.03043 (2018) - [i93]Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine:
SFV: Reinforcement Learning of Physical Skills from Videos. CoRR abs/1810.03599 (2018) - [i92]Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar:
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. CoRR abs/1810.06045 (2018) - [i91]Nicholas Rhinehart, Rowan McAllister, Sergey Levine:
Deep Imitative Models for Flexible Inference, Planning, and Control. CoRR abs/1810.06544 (2018) - [i90]Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine:
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation. CoRR abs/1810.07167 (2018) - [i89]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks. CoRR abs/1810.11043 (2018) - [i88]Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine:
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping. CoRR abs/1811.06964 (2018) - [i87]Dibya Ghosh, Abhishek Gupta, Sergey Levine:
Learning Actionable Representations with Goal-Conditioned Policies. CoRR abs/1811.07819 (2018) - [i86]John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, John DeNero, Pieter Abbeel, Sergey Levine:
Guiding Policies with Language via Meta-Learning. CoRR abs/1811.07882 (2018) - [i85]Reza Mahjourian, Navdeep Jaitly, Nevena Lazic, Sergey Levine, Risto Miikkulainen:
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play. CoRR abs/1811.12927 (2018) - [i84]Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex X. Lee, Sergey Levine:
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control. CoRR abs/1812.00568 (2018) - [i83]Ashish Kumar, Saurabh Gupta, David F. Fouhey, Sergey Levine, Jitendra Malik:
Visual Memory for Robust Path Following. CoRR abs/1812.00940 (2018) - [i82]Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine:
Residual Reinforcement Learning for Robot Control. CoRR abs/1812.03201 (2018) - [i81]Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic Algorithms and Applications. CoRR abs/1812.05905 (2018) - [i80]Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis:
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks. CoRR abs/1812.07252 (2018) - [i79]Anusha Nagabandi, Chelsea Finn, Sergey Levine:
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL. CoRR abs/1812.07671 (2018) - [i78]Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine:
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty. CoRR abs/1812.10687 (2018) - [i77]Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu:
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning. CoRR abs/1812.10972 (2018) - [i76]Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk via Deep Reinforcement Learning. CoRR abs/1812.11103 (2018) - 2017
- [c68]Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, Claire J. Tomlin:
Goal-driven dynamics learning via Bayesian optimization. CDC 2017: 5168-5173 - [c67]Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor, Julian Ibarz, Sergey Levine:
End-to-End Learning of Semantic Grasping. CoRL 2017: 119-132 - [c66]Connor Schenck, Jonathan Tompson, Sergey Levine, Dieter Fox:
Learning Robotic Manipulation of Granular Media. CoRL 2017: 239-248 - [c65]Roberto Calandra, Andrew Owens, Manu Upadhyaya, Wenzhen Yuan, Justin Lin, Edward H. Adelson, Sergey Levine:
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? CoRL 2017: 314-323 - [c64]Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine:
Self-Supervised Visual Planning with Temporal Skip Connections. CoRL 2017: 344-356 - [c63]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRL 2017: 357-368 - [c62]Pierre Sermanet, Corey Lynch, Jasmine Hsu, Sergey Levine:
Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation. CVPR Workshops 2017: 486-487 - [c61]Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik:
Cognitive Mapping and Planning for Visual Navigation. CVPR 2017: 7272-7281 - [c60]Avi Singh, Larry Yang, Sergey Levine:
GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images. ICCV 2017: 5852-5861 - [c59]Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. ICLR (Poster) 2017 - [c58]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. ICLR (Poster) 2017 - [c57]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR 2017 - [c56]Alex X. Lee, Sergey Levine, Pieter Abbeel:
Learning Visual Servoing with Deep Features and Fitted Q-Iteration. ICLR (Poster) 2017 - [c55]Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. ICLR (Poster) 2017 - [c54]Pierre Sermanet, Kelvin Xu, Sergey Levine:
Unsupervised Perceptual Rewards for Imitation Learning. ICLR (Workshop) 2017 - [c53]Jacob Andreas, Dan Klein, Sergey Levine:
Modular Multitask Reinforcement Learning with Policy Sketches. ICML 2017: 166-175 - [c52]Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine:
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. ICML 2017: 703-711 - [c51]Chelsea Finn, Pieter Abbeel, Sergey Levine:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017: 1126-1135 - [c50]Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine:
Reinforcement Learning with Deep Energy-Based Policies. ICML 2017: 1352-1361 - [c49]Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
Learning from the hindsight plan - Episodic MPC improvement. ICRA 2017: 336-343 - [c48]Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep reinforcement learning for tensegrity robot locomotion. ICRA 2017: 634-641 - [c47]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining self-supervised learning and imitation for vision-based rope manipulation. ICRA 2017: 2146-2153 - [c46]Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine:
Learning modular neural network policies for multi-task and multi-robot transfer. ICRA 2017: 2169-2176 - [c45]Chelsea Finn, Sergey Levine:
Deep visual foresight for planning robot motion. ICRA 2017: 2786-2793 - [c44]Gregory Kahn, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
PLATO: Policy learning using adaptive trajectory optimization. ICRA 2017: 3342-3349 - [c43]William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine:
Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states. ICRA 2017: 3373-3380 - [c42]Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine:
Path integral guided policy search. ICRA 2017: 3381-3388 - [c41]Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine:
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. ICRA 2017: 3389-3396 - [c40]Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel:
Value Iteration Networks. IJCAI 2017: 4949-4953 - [c39]Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine:
Collective robot reinforcement learning with distributed asynchronous guided policy search. IROS 2017: 79-86 - [c38]Justin Fu, John D. Co-Reyes, Sergey Levine:
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning. NIPS 2017: 2577-2587 - [c37]Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS 2017: 3846-3855 - [c36]Fereshteh Sadeghi, Sergey Levine:
CAD2RL: Real Single-Image Flight Without a Single Real Image. Robotics: Science and Systems 2017 - [c35]Pierre Sermanet, Kelvin Xu, Sergey Levine:
Unsupervised Perceptual Rewards for Imitation Learning. Robotics: Science and Systems 2017 - [i75]Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, Sergey Levine:
Uncertainty-Aware Reinforcement Learning for Collision Avoidance. CoRR abs/1702.01182 (2017) - [i74]Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik:
Cognitive Mapping and Planning for Visual Navigation. CoRR abs/1702.03920 (2017) - [i73]Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine:
Reinforcement Learning with Deep Energy-Based Policies. CoRR abs/1702.08165 (2017) - [i72]Justin Fu, John D. Co-Reyes, Sergey Levine:
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning. CoRR abs/1703.01260 (2017) - [i71]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation. CoRR abs/1703.02018 (2017) - [i70]Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. CoRR abs/1703.02949 (2017) - [i69]Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine:
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. CoRR abs/1703.03078 (2017) - [i68]Chelsea Finn, Pieter Abbeel, Sergey Levine:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. CoRR abs/1703.03400 (2017) - [i67]Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, Claire J. Tomlin:
Goal-Driven Dynamics Learning via Bayesian Optimization. CoRR abs/1703.09260 (2017) - [i66]Alex X. Lee, Sergey Levine, Pieter Abbeel:
Learning Visual Servoing with Deep Features and Fitted Q-Iteration. CoRR abs/1703.11000 (2017) - [i65]Pierre Sermanet, Corey Lynch, Jasmine Hsu, Sergey Levine:
Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation. CoRR abs/1704.06888 (2017) - [i64]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR abs/1706.00387 (2017) - [i63]Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor, Julian Ibarz, Sergey Levine:
End-to-End Learning of Semantic Grasping. CoRR abs/1707.01932 (2017) - [i62]Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine:
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration. CoRR abs/1707.02920 (2017) - [i61]Yuxuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation. CoRR abs/1707.03374 (2017) - [i60]Avi Singh, Larry Yang, Sergey Levine:
GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images. CoRR abs/1708.02313 (2017) - [i59]Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine:
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning. CoRR abs/1708.02596 (2017) - [i58]Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Deep Object-Centric Representations for Generalizable Robot Learning. CoRR abs/1708.04225 (2017) - [i57]Connor Schenck, Jonathan Tompson, Dieter Fox, Sergey Levine:
Learning Robotic Manipulation of Granular Media. CoRR abs/1709.02833 (2017) - [i56]Somil Bansal, Roberto Calandra, Sergey Levine, Claire J. Tomlin:
MBMF: Model-Based Priors for Model-Free Reinforcement Learning. CoRR abs/1709.03153 (2017) - [i55]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRR abs/1709.04905 (2017) - [i54]Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke:
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping. CoRR abs/1709.07857 (2017) - [i53]Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine:
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR abs/1709.10087 (2017) - [i52]Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine:
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. CoRR abs/1709.10489 (2017) - [i51]Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine:
Self-Supervised Visual Planning with Temporal Skip Connections. CoRR abs/1710.05268 (2017) - [i50]Roberto Calandra, Andrew Owens, Manu Upadhyaya, Wenzhen Yuan, Justin Lin, Edward H. Adelson, Sergey Levine:
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? CoRR abs/1710.05512 (2017) - [i49]Justin Fu, Katie Luo, Sergey Levine:
Learning Robust Rewards with Adversarial Inverse Reinforcement Learning. CoRR abs/1710.11248 (2017) - [i48]Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine:
Stochastic Variational Video Prediction. CoRR abs/1710.11252 (2017) - [i47]Peter H. Jin, Sergey Levine, Kurt Keutzer:
Regret Minimization for Partially Observable Deep Reinforcement Learning. CoRR abs/1710.11424 (2017) - [i46]Chelsea Finn, Sergey Levine:
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm. CoRR abs/1710.11622 (2017) - [i45]Jacob Andreas, Dan Klein, Sergey Levine:
Learning with Latent Language. CoRR abs/1711.00482 (2017) - [i44]Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Gregory Kahn, Sergey Levine, Ronald S. Fearing:
Neural Network Dynamics Models for Control of Under-actuated Legged Millirobots. CoRR abs/1711.05253 (2017) - [i43]Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine:
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. CoRR abs/1711.06782 (2017) - [i42]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. CoRR abs/1711.09874 (2017) - [i41]Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine:
Sim2Real View Invariant Visual Servoing by Recurrent Control. CoRR abs/1712.07642 (2017) - [i40]Saurabh Gupta, David F. Fouhey, Sergey Levine, Jitendra Malik:
Unifying Map and Landmark Based Representations for Visual Navigation. CoRR abs/1712.08125 (2017) - 2016
- [j5]Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel:
End-to-End Training of Deep Visuomotor Policies. J. Mach. Learn. Res. 17: 39:1-39:40 (2016) - [c34]Chelsea Finn, Sergey Levine, Pieter Abbeel:
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. ICML 2016: 49-58 - [c33]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. ICML 2016: 2829-2838 - [c32]Vikash Kumar, Emanuel Todorov, Sergey Levine:
Optimal control with learned local models: Application to dexterous manipulation. ICRA 2016: 378-383 - [c31]Chris Xie, Sachin Patil, Teodor Mihai Moldovan, Sergey Levine, Pieter Abbeel:
Model-based reinforcement learning with parametrized physical models and optimism-driven exploration. ICRA 2016: 504-511 - [c30]Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel:
Deep spatial autoencoders for visuomotor learning. ICRA 2016: 512-519 - [c29]Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel:
Learning deep neural network policies with continuous memory states. ICRA 2016: 520-527 - [c28]Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel:
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search. ICRA 2016: 528-535 - [c27]Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel:
Learning dexterous manipulation for a soft robotic hand from human demonstrations. IROS 2016: 3786-3793 - [c26]Justin Fu, Sergey Levine, Pieter Abbeel:
One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. IROS 2016: 4019-4026 - [c25]Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen:
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection. ISER 2016: 173-184 - [c24]Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. NIPS 2016: 64-72 - [c23]Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, Garrett Thomas:
Value Iteration Networks. NIPS 2016: 2146-2154 - [c22]William H. Montgomery, Sergey Levine:
Guided Policy Search via Approximate Mirror Descent. NIPS 2016: 4008-4016 - [c21]Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel:
Backprop KF: Learning Discriminative Deterministic State Estimators. NIPS 2016: 4376-4384 - [c20]Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell:
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints. WAFR 2016: 688-703 - [c19]Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik:
Learning Visual Predictive Models of Physics for Playing Billiards. ICLR (Poster) 2016 - [c18]Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih:
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) 2016 - [c17]John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) 2016 - [i39]Aviv Tamar, Sergey Levine, Pieter Abbeel:
Value Iteration Networks. CoRR abs/1602.02867 (2016) - [i38]Chelsea Finn, Sergey Levine, Pieter Abbeel:
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. CoRR abs/1603.00448 (2016) - [i37]Gregory Kahn, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
PLATO: Policy Learning using Adaptive Trajectory Optimization. CoRR abs/1603.00622 (2016) - [i36]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. CoRR abs/1603.00748 (2016) - [i35]Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen:
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection. CoRR abs/1603.02199 (2016) - [i34]Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel:
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration. CoRR abs/1603.06348 (2016) - [i33]Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel:
Backprop KF: Learning Discriminative Deterministic State Estimators. CoRR abs/1605.07148 (2016) - [i32]Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. CoRR abs/1605.07157 (2016) - [i31]Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Learning to Poke by Poking: Experiential Learning of Intuitive Physics. CoRR abs/1606.07419 (2016) - [i30]William Montgomery, Sergey Levine:
Guided Policy Search as Approximate Mirror Descent. CoRR abs/1607.04614 (2016) - [i29]Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine:
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer. CoRR abs/1609.07088 (2016) - [i28]Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
Learning from the Hindsight Plan - Episodic MPC Improvement. CoRR abs/1609.09001 (2016) - [i27]Xinyang Geng, Marvin Zhang, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep Reinforcement Learning for Tensegrity Robot Locomotion. CoRR abs/1609.09049 (2016) - [i26]Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine:
Path Integral Guided Policy Search. CoRR abs/1610.00529 (2016) - [i25]Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine:
Deep Reinforcement Learning for Robotic Manipulation. CoRR abs/1610.00633 (2016) - [i24]Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine:
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search. CoRR abs/1610.00673 (2016) - [i23]Chelsea Finn, Sergey Levine:
Deep Visual Foresight for Planning Robot Motion. CoRR abs/1610.00696 (2016) - [i22]William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine:
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States. CoRR abs/1610.01112 (2016) - [i21]Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, Balaraman Ravindran:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. CoRR abs/1610.01283 (2016) - [i20]Jacob Andreas, Dan Klein, Sergey Levine:
Modular Multitask Reinforcement Learning with Policy Sketches. CoRR abs/1611.01796 (2016) - [i19]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR abs/1611.02247 (2016) - [i18]Chelsea Finn, Paul F. Christiano, Pieter Abbeel, Sergey Levine:
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models. CoRR abs/1611.03852 (2016) - [i17]Fereshteh Sadeghi, Sergey Levine:
(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image. CoRR abs/1611.04201 (2016) - [i16]Vikash Kumar, Abhishek Gupta, Emanuel Todorov, Sergey Levine:
Learning Dexterous Manipulation Policies from Experience and Imitation. CoRR abs/1611.05095 (2016) - [i15]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. CoRR abs/1612.00429 (2016) - [i14]Pierre Sermanet, Kelvin Xu, Sergey Levine:
Unsupervised Perceptual Rewards for Imitation Learning. CoRR abs/1612.06699 (2016) - 2015
- [c16]Katerina Fragkiadaki, Sergey Levine, Panna Felsen, Jitendra Malik:
Recurrent Network Models for Human Dynamics. ICCV 2015: 4346-4354 - [c15]John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz:
Trust Region Policy Optimization. ICML 2015: 1889-1897 - [c14]Sergey Levine, Nolan Wagener, Pieter Abbeel:
Learning contact-rich manipulation skills with guided policy search. ICRA 2015: 156-163 - [c13]Alex X. Lee, Henry Lu, Abhishek Gupta, Sergey Levine, Pieter Abbeel:
Learning force-based manipulation of deformable objects from multiple demonstrations. ICRA 2015: 177-184 - [c12]Teodor Mihai Moldovan, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
Optimism-driven exploration for nonlinear systems. ICRA 2015: 3239-3246 - [c11]Alex X. Lee, Abhishek Gupta, Henry Lu, Sergey Levine, Pieter Abbeel:
Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation. IROS 2015: 5265-5272 - [c10]Weiqiao Han, Sergey Levine, Pieter Abbeel:
Learning compound multi-step controllers under unknown dynamics. IROS 2015: 6435-6442 - [i13]Sergey Levine, Nolan Wagener, Pieter Abbeel:
Learning Contact-Rich Manipulation Skills with Guided Policy Search. CoRR abs/1501.05611 (2015) - [i12]John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel:
Trust Region Policy Optimization. CoRR abs/1502.05477 (2015) - [i11]Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel:
End-to-End Training of Deep Visuomotor Policies. CoRR abs/1504.00702 (2015) - [i10]Bradly C. Stadie, Sergey Levine, Pieter Abbeel:
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models. CoRR abs/1507.00814 (2015) - [i9]Marvin Zhang, Sergey Levine, Zoe McCarthy, Chelsea Finn, Pieter Abbeel:
Policy Learning with Continuous Memory States for Partially Observed Robotic Control. CoRR abs/1507.01273 (2015) - [i8]Katerina Fragkiadaki, Sergey Levine, Jitendra Malik:
Recurrent Network Models for Kinematic Tracking. CoRR abs/1508.00271 (2015) - [i7]Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel:
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders. CoRR abs/1509.06113 (2015) - [i6]Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel:
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search. CoRR abs/1509.06791 (2015) - [i5]Christopher Xie, Sachin Patil, Teodor Mihai Moldovan, Sergey Levine, Pieter Abbeel:
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration. CoRR abs/1509.06824 (2015) - [i4]Justin Fu, Sergey Levine, Pieter Abbeel:
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors. CoRR abs/1509.06841 (2015) - [i3]Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Xingchao Peng, Sergey Levine, Kate Saenko, Trevor Darrell:
Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments. CoRR abs/1511.07111 (2015) - 2014
- [b1]Sergey Levine:
Motor skill learning with local trajectory methods. Stanford University, USA, 2014 - [c9]Travis Mandel, Yun-En Liu, Sergey Levine, Emma Brunskill, Zoran Popovic:
Offline policy evaluation across representations with applications to educational games. AAMAS 2014: 1077-1084 - [c8]Sergey Levine, Vladlen Koltun:
Learning Complex Neural Network Policies with Trajectory Optimization. ICML 2014: 829-837 - [c7]Sergey Levine, Pieter Abbeel:
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. NIPS 2014: 1071-1079 - 2013
- [c6]Sergey Levine, Vladlen Koltun:
Guided Policy Search. ICML (3) 2013: 1-9 - [c5]Sergey Levine, Vladlen Koltun:
Variational Policy Search via Trajectory Optimization. NIPS 2013: 207-215 - [i2]Sergey Levine:
Exploring Deep and Recurrent Architectures for Optimal Control. CoRR abs/1311.1761 (2013) - 2012
- [j4]Sergey Levine, Jack M. Wang, Alexis Haraux, Zoran Popovic, Vladlen Koltun:
Continuous character control with low-dimensional embeddings. ACM Trans. Graph. 31(4): 28:1-28:10 (2012) - [c4]Sergey Levine, Vladlen Koltun:
Continuous Inverse Optimal Control with Locally Optimal Examples. ICML 2012 - [c3]Sergey Levine, Jovan Popovic:
Physically Plausible Simulation for Character Animation. Symposium on Computer Animation 2012: 221-230 - [i1]Sergey Levine, Vladlen Koltun:
Continuous Inverse Optimal Control with Locally Optimal Examples. CoRR abs/1206.4617 (2012) - 2011
- [j3]Sergey Levine, Yongjoon Lee, Vladlen Koltun, Zoran Popovic:
Space-time planning with parameterized locomotion controllers. ACM Trans. Graph. 30(3): 23:1-23:11 (2011) - [c2]Sergey Levine, Zoran Popovic, Vladlen Koltun:
Nonlinear Inverse Reinforcement Learning with Gaussian Processes. NIPS 2011: 19-27 - 2010
- [j2]Sergey Levine, Philipp Krähenbühl, Sebastian Thrun, Vladlen Koltun:
Gesture controllers. ACM Trans. Graph. 29(4): 124:1-124:11 (2010) - [c1]Sergey Levine, Zoran Popovic, Vladlen Koltun:
Feature Construction for Inverse Reinforcement Learning. NIPS 2010: 1342-1350
2000 – 2009
- 2009
- [j1]Sergey Levine, Christian Theobalt, Vladlen Koltun:
Real-time prosody-driven synthesis of body language. ACM Trans. Graph. 28(5): 172 (2009)
Coauthor Index
[c403] [c398] [c390] [c381] [i474] [i473] [i470] [i464] [i460] [c332] [i423] [i408] [i356] [i333] [j18] [j16] [j15] [c239] [i308] [i306] [c178] [c171] [i257] [i222] [c159] [c148] [c146] [c140] [c134] [c131] [c129] [c127] [c121] [i199] [i187] [i181] [i167] [i151] [i147] [j7] [j6] [c112] [c97] [c96] [c95] [c94] [c92] [c90] [c89] [c85] [c83] [c75] [c69] [i139] [i137] [i133] [i127] [i126] [i122] [i121] [i120] [i119] [i111] [i102] [i99] [i93] [i90] [i89] [i86] [i81] [c63] [c59] [c58] [c56] [c51] [c50] [c49] [c48] [c47] [c46] [c44] [c43] [c40] [i75] [i73] [i71] [i70] [i68] [i66] [i61] [i58] [i55] [i52] [j5] [c34] [c31] [c30] [c29] [c28] [c27] [c26] [c23] [c21] [c20] [c17] [i39] [i38] [i37] [i34] [i33] [i31] [i29] [i28] [i27] [i22] [i18] [i15] [c15] [c14] [c13] [c12] [c11] [c10] [i13] [i12] [i11] [i10] [i9] [i7] [i6] [i5] [i4] [c7]
Coline Devin
aka: Coline Manon Devin
aka: Coline Manon Devin
Frederik Ebert
aka: Frederik D. Ebert
aka: Frederik D. Ebert
Benjamin Eysenbach
aka: Ben Eysenbach
aka: Ben Eysenbach
[c393] [c389] [i458] [i442] [c366] [c362] [c358] [c345] [c339] [i427] [i406] [i401] [i400] [i399] [c318] [c317] [c316] [c307] [c286] [c285] [c284] [c281] [i355] [i354] [i353] [i341] [c273] [c263] [c262] [c258] [c253] [c251] [c235] [c228] [c221] [i311] [i310] [i305] [i304] [i282] [i277] [i276] [i270] [i259] [c184] [i255] [i231] [i210] [i204] [i203] [c158] [c126] [c120] [i172] [i171] [i158] [i148] [i144] [c107] [c96] [i134] [i111] [i109] [i43]
[c400] [c399] [c388] [c384] [c381] [c379] [c377] [i472] [i471] [i468] [i463] [i456] [i452] [i450] [i446] [i441] [i439] [i438] [i433] [c374] [c372] [c368] [c362] [c345] [c340] [c336] [c333] [c329] [i427] [i424] [i419] [i411] [i405] [i399] [i398] [i397] [i394] [i390] [i386] [i382] [c326] [c320] [c311] [c309] [c300] [c287] [c279] [c278] [i376] [i375] [i374] [i368] [i338] [i335] [i322] [j19] [c275] [c274] [c272] [c266] [c253] [c251] [c247] [c245] [c230] [c229] [c226] [c225] [c220] [i315] [i313] [i304] [i303] [i294] [i285] [i281] [i280] [i278] [i272] [i264] [i263] [i261] [i260] [c217] [c214] [c213] [c210] [c205] [c204] [c199] [c198] [c192] [c191] [c190] [c189] [c181] [c177] [c176] [c175] [c174] [i258] [i254] [i253] [i247] [i240] [i238] [i232] [i227] [i225] [i224] [i220] [i219] [i212] [i211] [i207] [i206] [i205] [i203] [c169] [c167] [c163] [c153] [c152] [c148] [c147] [c145] [c142] [c141] [c139] [c131] [c130] [c127] [c126] [c117] [c116] [i197] [i195] [i194] [i190] [i189] [i187] [i186] [i185] [i174] [i164] [i156] [i155] [i152] [i149] [i148] [i146] [i143] [c115] [c111] [c108] [c106] [c102] [c97] [c92] [c84] [c72] [c69] [i138] [i137] [i130] [i126] [i122] [i121] [i112] [i110] [i109] [i100] [i98] [i95] [i94] [i89] [i84] [i79] [i77] [c64] [c63] [c58] [c51] [c45] [c43] [i68] [i55] [i51] [i48] [i46] [j5] [c34] [c30] [c29] [c24] [c20] [i38] [i32] [i23] [i22] [i18] [i15] [i11] [i9] [i7] [i3]
Peter R. Florence
aka: Pete Florence
aka: Pete Florence
Joseph Gonzalez 0001
aka: Joseph E. Gonzalez
aka: Joseph E. Gonzalez
Shixiang Gu
aka: Shixiang Shane Gu
aka: Shixiang Shane Gu
[c381] [c379] [i471] [c373] [c351] [c350] [i396] [c309] [c289] [c288] [i369] [i340] [i334] [i320] [c276] [c258] [c246] [c236] [c226] [c225] [c223] [i300] [i295] [i287] [i285] [i284] [i260] [c197] [c182] [c175] [i258] [i252] [i244] [i236] [i234] [i227] [c168] [c164] [c160] [c159] [c158] [c156] [c135] [c127] [c126] [i187] [i161] [i153] [i148] [i144] [c96] [c89] [c75] [c71] [i134] [i133] [i111] [i109] [i105] [i92] [i87] [i86] [i81] [c59] [c46] [i70] [i61] [i53] [c27] [i34] [i29] [i16] [c13] [c11]
[c392] [c388] [c381] [i472] [i468] [c372] [c368] [c359] [c354] [c351] [c342] [c336] [c335] [c330] [i422] [i420] [i410] [i398] [i394] [i377] [c326] [c322] [c309] [c300] [i375] [i369] [i368] [i367] [i346] [i327] [i322] [c274] [c271] [c251] [c229] [c226] [i304] [i303] [i285] [i281] [i266] [i260] [c213] [c201] [c200] [c175] [c172] [i258] [i249] [i247] [i243] [c168] [c167] [i166] [i156] [i153] [c52] [i69]
Alex Irpan
aka: Alexander Irpan
aka: Alexander Irpan
[c400] [c391] [c383] [c378] [c377] [i469] [i462] [i459] [i457] [i445] [i444] [i433] [c375] [c368] [c365] [c364] [c363] [c340] [c338] [c333] [i419] [i413] [i394] [i393] [i388] [i386] [i385] [c323] [c314] [c313] [c312] [c301] [c300] [c283] [c280] [i375] [i372] [i366] [i347] [i338] [i330] [i329] [i328] [i326] [i324] [c275] [c267] [c265] [c261] [c255] [c241] [c229] [c224] [c220] [i313] [i307] [i289] [i288] [i281] [i280] [i271] [i262] [c212] [c182] [c181] [c180] [c179] [i252] [i248] [i241] [i239] [i221] [i219] [i217] [i216] [i215] [c144] [c124] [i196] [i176] [i159] [i141] [i140]
Timothy P. Lillicrap
aka: Tim Lillicrap
aka: Tim Lillicrap
Rowan McAllister
aka: Rowan Thomas McAllister
aka: Rowan Thomas McAllister
Vitchyr Pong
aka: Vitchyr H. Pong
aka: Vitchyr H. Pong
Nicholas Rhinehart
aka: Nick Rhinehart
aka: Nick Rhinehart
Ruslan Salakhutdinov
aka: Russ Salakhutdinov
aka: Russ Salakhutdinov
Homer Walke
aka: Homer Rich Walke
aka: Homer Rich Walke
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