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2020 – today
- 2024
[j17]Chenjia Bai
, Ting Xiao, Zhoufan Zhu, Lingxiao Wang, Fan Zhou, Animesh Garg, Bin He, Peng Liu, Zhaoran Wang:
Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 35(7): 8954-8968 (2024)- [c97]Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden, Animesh Garg:
Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation. ICML 2024 - [c96]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 - [c95]Shutong Zhang, Yi-Ling Qiao, Guanglei Zhu, Eric Heiden, Dylan Turpin, Jingzhou Liu, Ming C. Lin, Miles Macklin, Animesh Garg:
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors. ICRA 2024: 13983-13990 - [c94]Qinxi Yu, Masoud Moghani, Karthik Dharmarajan, Vincent Schorp, William Chung-Ho Panitch, Jingzhou Liu, Kush Hari, Huang Huang, Mayank Mittal, Ken Goldberg, Animesh Garg:
Orbit-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity. ICRA 2024: 15509-15516 - [i115]Marta Skreta, Zihan Zhou, Jia Lin Yuan, Kourosh Darvish, Alán Aspuru-Guzik, Animesh Garg:
RePLan: Robotic Replanning with Perception and Language Models. CoRR abs/2401.04157 (2024) - [i114]Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, Florian Shkurti:
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization. CoRR abs/2401.06949 (2024) - [i113]Nick Walker, Xuning Yang, Animesh Garg, Maya Cakmak, Dieter Fox, Claudia Pérez-D'Arpino:
Fast Explicit-Input Assistance for Teleoperation in Clutter. CoRR abs/2402.02612 (2024) - [i112]Zihan Zhou, Jonathan Booher, Wei Liu, Aleksandr Petiushko, Animesh Garg:
Multi-Constraint Safe RL with Objective Suppression for Safety-Critical Applications. CoRR abs/2402.15650 (2024) - [i111]Tongzhou Mu, Yijie Guo, Jie Xu, Ankit Goyal, Hao Su, Dieter Fox, Animesh Garg:
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent. CoRR abs/2404.07428 (2024) - [i110]Qinxi Yu, Masoud Moghani, Karthik Dharmarajan, Vincent Schorp, William Chung-Ho Panitch, Jingzhou Liu, Kush Hari, Huang Huang, Mayank Mittal, Ken Goldberg, Animesh Garg:
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity. CoRR abs/2404.16027 (2024) - [i109]Masoud Moghani, Lars Doorenbos, William Chung-Ho Panitch, Sean Huver, Mahdi Azizian, Ken Goldberg, Animesh Garg:
SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants. CoRR abs/2405.05226 (2024) - [i108]Weiyu Liu, Jiayuan Mao, Joy Hsu, Tucker Hermans, Animesh Garg, Jiajun Wu:
Composable Part-Based Manipulation. CoRR abs/2405.05876 (2024) - [i107]Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden, Animesh Garg:
Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation. CoRR abs/2405.17784 (2024) - [i106]Ignat Georgiev, Varun Giridhar, Nicklas Hansen, Animesh Garg:
PWM: Policy Learning with Large World Models. CoRR abs/2407.02466 (2024) - [i105]Atharva Mete, Haotian Xue, Albert Wilcox, Yongxin Chen, Animesh Garg:
QueST: Self-Supervised Skill Abstractions for Learning Continuous Control. CoRR abs/2407.15840 (2024) - 2023
- [j16]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: a differentiable simulator for parameter inference and control in robotic cutting. Auton. Robots 47(5): 549-578 (2023) - [j15]Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg:
ProgPrompt: program generation for situated robot task planning using large language models. Auton. Robots 47(8): 999-1012 (2023) - [j14]Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Large language models for chemistry robotics. Auton. Robots 47(8): 1057-1086 (2023) - [j13]Karthik Dharmarajan, Will Panitch, Baiyu Shi, Huang Huang, Lawrence Yunliang Chen, Masoud Moghani, Qinxi Yu, Kush Hari, Thomas Low, Danyal Fer, Animesh Garg, Ken Goldberg:
Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot. J. Medical Robotics Res. 8(3&4): 2340006:1-2340006:15 (2023) - [j12]Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters:
Continuous-Time Fitted Value Iteration for Robust Policies. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 5534-5548 (2023) - [j11]Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg:
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics Autom. Lett. 8(6): 3740-3747 (2023) - [j10]Andrew Melnik, Robin Schiewer, Moritz Lange, Andrei Ioan Muresanu, Mozhgan Saeidi, Animesh Garg, Helge J. Ritter:
Benchmarks for Physical Reasoning AI. Trans. Mach. Learn. Res. 2023 (2023) - [c93]Maria Attarian, Muhammad Adil Asif, Jingzhou Liu, Ruthrash Hari, Animesh Garg, Igor Gilitschenski, Jonathan Tompson:
Geometry Matching for Multi-Embodiment Grasping. CoRL 2023: 1242-1256 - [c92]Weiyu Liu, Jiayuan Mao, Joy Hsu, Tucker Hermans, Animesh Garg, Jiajun Wu:
Composable Part-Based Manipulation. CoRL 2023: 1300-1315 - [c91]Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, Animesh Garg:
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models. ICLR 2023 - [c90]Zihan Zhou, Animesh Garg:
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward. ICLR 2023 - [c89]Yi Ru Wang, Yuchi Zhao, Haoping Xu, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
MVTrans: Multi-View Perception of Transparent Objects. ICRA 2023: 3771-3778 - [c88]Liquan Wang, Nikita Dvornik, Rafael Dubeau, Mayank Mittal, Animesh Garg:
Self-Supervised Learning of Action Affordances as Interaction Modes. ICRA 2023: 7279-7286 - [c87]Dylan Turpin, Tao Zhong, Shutong Zhang, Guanglei Zhu, Eric Heiden, Miles Macklin, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation. ICRA 2023: 8082-8089 - [c86]Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea Tagliasacchi:
nerf2nerf: Pairwise Registration of Neural Radiance Fields. ICRA 2023: 9354-9361 - [c85]Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg:
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models. ICRA 2023: 11523-11530 - [c84]Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, Animesh Garg:
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models. NeurIPS 2023 - [i104]Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Pooria Poorsarvi Tehrani, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg:
ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments. CoRR abs/2301.04195 (2023) - [i103]Yi Ru Wang, Yuchi Zhao, Haoping Xu, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
MVTrans: Multi-View Perception of Transparent Objects. CoRR abs/2302.11683 (2023) - [i102]Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Kourosh Darvish, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting. CoRR abs/2303.14100 (2023) - [i101]Chaitanya Devaguptapu, Samarth Sinha, K. J. Joseph, Vineeth N. Balasubramanian, Animesh Garg:
Δ-Networks for Efficient Model Patching. CoRR abs/2303.14772 (2023) - [i100]Nikita Dvornik, Isma Hadji, Ran Zhang, Konstantinos G. Derpanis, Animesh Garg, Richard P. Wildes, Allan D. Jepson:
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos. CoRR abs/2304.13265 (2023) - [i99]Zihan Zhou, Animesh Garg:
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward. CoRR abs/2305.00508 (2023) - [i98]Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, Animesh Garg:
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models. CoRR abs/2305.11281 (2023) - [i97]Liquan Wang, Nikita Dvornik, Rafael Dubeau, Mayank Mittal, Animesh Garg:
Self-Supervised Learning of Action Affordances as Interaction Modes. CoRR abs/2305.17565 (2023) - [i96]Dylan Turpin, Tao Zhong, Shutong Zhang, Guanglei Zhu, Jingzhou Liu, Ritvik Singh, Eric Heiden, Miles Macklin, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation. CoRR abs/2306.08132 (2023) - [i95]Shutong Zhang, Yi-Ling Qiao, Guanglei Zhu, Eric Heiden, Dylan Turpin, Jingzhou Liu, Ming C. Lin, Miles Macklin, Animesh Garg:
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors. CoRR abs/2311.16552 (2023) - [i94]Maria Attarian, Muhammad Adil Asif, Jingzhou Liu, Ruthrash Hari, Animesh Garg, Igor Gilitschenski, Jonathan Tompson:
Geometry Matching for Multi-Embodiment Grasping. CoRR abs/2312.03864 (2023) - [i93]Andrew Melnik, Robin Schiewer, Moritz Lange, Andrei Ioan Muresanu, Mozhgan Saeidi, Animesh Garg, Helge J. Ritter:
Benchmarks for Physical Reasoning AI. CoRR abs/2312.10728 (2023) - 2022
- [j9]Dylan P. Losey, Hong Jun Jeon, Mengxi Li, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Jeannette Bohg, Dorsa Sadigh:
Learning latent actions to control assistive robots. Auton. Robots 46(1): 115-147 (2022) - [j8]Aysegul Dundar, Kevin J. Shih, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro:
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3883-3894 (2022) - [j7]Jiankai Sun, De-An Huang, Bo Lu, Yun-Hui Liu, Bolei Zhou, Animesh Garg:
PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks. IEEE Robotics Autom. Lett. 7(2): 4924-4930 (2022) - [c83]Matthew Shunshi Zhang, Murat A. Erdogdu, Animesh Garg:
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. AAAI 2022: 9066-9073 - [c82]Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. AAMAS 2022: 1500-1508 - [c81]Haoyu Xiong, Haoyuan Fu, Jieyi Zhang, Chen Bao, Qiang Zhang, Yongxi Huang, Wenqiang Xu, Animesh Garg, Cewu Lu:
RoboTube: Learning Household Manipulation from Human Videos with Simulated Twin Environments. CoRL 2022: 1-10 - [c80]Krishna Murthy Jatavallabhula, Miles Macklin, Dieter Fox, Animesh Garg, Fabio Ramos:
Bayesian Object Models for Robotic Interaction with Differentiable Probabilistic Programming. CoRL 2022: 1563-1574 - [c79]Wei Yu, Wenxin Chen, Songheng Yin, Steve Easterbrook, Animesh Garg:
Modular Action Concept Grounding in Semantic Video Prediction. CVPR 2022: 3595-3604 - [c78]Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Zeynep Akata, Hugo Larochelle, Animesh Garg:
Uniform Priors for Data-Efficient Learning. CVPR Workshops 2022: 4016-4027 - [c77]Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu:
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CVPR 2022: 4996-5005 - [c76]Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg:
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors. CVPR 2022: 12714-12723 - [c75]Dylan Turpin, Liquan Wang, Eric Heiden, Yun-Chun Chen, Miles Macklin, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
Grasp'D: Differentiable Contact-Rich Grasp Synthesis for Multi-Fingered Hands. ECCV (6) 2022: 201-221 - [c74]Jie Xu, Viktor Makoviychuk, Yashraj S. Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin:
Accelerated Policy Learning with Parallel Differentiable Simulation. ICLR 2022 - [c73]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. ICLR 2022 - [c72]Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand:
Value Gradient weighted Model-Based Reinforcement Learning. ICLR 2022 - [c71]Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara:
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. ICML 2022: 23645-23667 - [c70]Mayank Mittal, David Hoeller, Farbod Farshidian, Marco Hutter, Animesh Garg:
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation. IROS 2022: 1647-1654 - [c69]Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Ankur Handa, Animesh Garg:
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger. IROS 2022: 11802-11809 - [c68]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. L4DC 2022: 110-123 - [c67]Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg:
MoCoDA: Model-based Counterfactual Data Augmentation. NeurIPS 2022 - [c66]Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson:
Breaking Bad: A Dataset for Geometric Fracture and Reassembly. NeurIPS 2022 - [c65]Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg:
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. NeurIPS 2022 - [c64]Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel van de Panne:
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model. WAFR 2022: 523-539 - [i92]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. CoRR abs/2202.11566 (2022) - [i91]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting. CoRR abs/2203.10263 (2022) - [i90]Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guang Wei Yu:
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CoRR abs/2203.15086 (2022) - [i89]Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand:
Value Gradient weighted Model-Based Reinforcement Learning. CoRR abs/2204.01464 (2022) - [i88]Jie Xu, Viktor Makoviychuk, Yashraj S. Narang, Fabio T. Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin:
Accelerated Policy Learning with Parallel Differentiable Simulation. CoRR abs/2204.07137 (2022) - [i87]Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg:
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors. CoRR abs/2205.14886 (2022) - [i86]Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg:
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. CoRR abs/2206.08851 (2022) - [i85]Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam, Wei Yang, Animesh Garg, Dieter Fox:
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions. CoRR abs/2206.14854 (2022) - [i84]Dylan Turpin, Liquan Wang, Eric Heiden, Yun-Chun Chen, Miles Macklin, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands. CoRR abs/2208.12250 (2022) - [i83]Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg:
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models. CoRR abs/2209.11302 (2022) - [i82]Maria Attarian, Advaya Gupta, Ziyi Zhou, Wei Yu, Igor Gilitschenski, Animesh Garg:
See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction. CoRR abs/2210.03825 (2022) - [i81]Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, Animesh Garg:
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models. CoRR abs/2210.05861 (2022) - [i80]Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg:
MoCoDA: Model-based Counterfactual Data Augmentation. CoRR abs/2210.11287 (2022) - [i79]Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson:
Breaking Bad: A Dataset for Geometric Fracture and Reassembly. CoRR abs/2210.11463 (2022) - [i78]Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea Tagliasacchi:
nerf2nerf: Pairwise Registration of Neural Radiance Fields. CoRR abs/2211.01600 (2022) - [i77]Amir Rasouli, Randy Goebel, Matthew E. Taylor, Iuliia Kotseruba, Soheil Alizadeh, Tianpei Yang, Montgomery Alban, Florian Shkurti, Yuzheng Zhuang, Adam Scibior, Kasra Rezaee, Animesh Garg, David Meger, Jun Luo, Liam Paull, Weinan Zhang, Xinyu Wang, Xi Chen:
NeurIPS 2022 Competition: Driving SMARTS. CoRR abs/2211.07545 (2022) - [i76]Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish, Yuchi Zhao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, Florian Shkurti:
An Adaptive Robotics Framework for Chemistry Lab Automation. CoRR abs/2212.09672 (2022) - [i75]Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Animesh Garg, Zhaoran Wang, Lihong Li, Doina Precup:
Offline Policy Optimization in RL with Variance Regularizaton. CoRR abs/2212.14405 (2022) - 2021
- [c63]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. AAAI 2021: 9666-9674 - [c62]Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, Yoav Artzi:
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution. CoRL 2021: 706-717 - [c61]Haoping Xu, Yi Ru Wang, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects. CoRL 2021: 827-838 - [c60]Samarth Sinha, Ajay Mandlekar, Animesh Garg:
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning in Robotics. CoRL 2021: 907-917 - [c59]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. ICLR 2021 - [c58]Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg:
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. ICLR 2021 - [c57]Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti:
Latent Skill Planning for Exploration and Transfer. ICLR 2021 - [c56]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. ICML 2021: 577-587 - [c55]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar:
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML 2021: 6860-6870 - [c54]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. ICML 2021: 7224-7234 - [c53]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. ICML 2021: 7301-7312 - [c52]Homanga Bharadhwaj, Animesh Garg, Florian Shkurti:
LEAF: Latent Exploration Along the Frontier. ICRA 2021: 677-684 - [c51]Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, Animesh Garg:
Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. ICRA 2021: 4955-4961 - [c50]Arthur Allshire, Roberto Martín-Martín, Charles Lin, Shawn Manuel, Silvio Savarese, Animesh Garg:
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning. ICRA 2021: 6650-6656 - [c49]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. ICRA 2021: 7540-7547 - [c48]Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg:
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos. IROS 2021: 7827-7834 - [c47]Stefan Bauer, Manuel Wüthrich, Felix Widmaier, Annika Buchholz, Sebastian Stark, Anirudh Goyal, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vincent Berenz, Vaibhav Agrawal, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Takahiro Maeda, Harshit Sikchi, Jilong Wang, Qingfeng Yao, Shuyu Yang, Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond, Bernhard Schölkopf:
Real Robot Challenge: A Robotics Competition in the Cloud. NeurIPS (Competition and Demos) 2021: 190-204 - [c46]Michael Poli, Stefano Massaroli, Luca Scimeca, Sanghyuk Chun, Seong Joon Oh, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg:
Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions. NeurIPS 2021: 9977-9989 - [c45]Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson:
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers. NeurIPS 2021: 13782-13793 - [c44]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. NeurIPS 2021: 17007-17020 - [c43]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting. Robotics: Science and Systems 2021 - [c42]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. Robotics: Science and Systems 2021 - [c41]Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels. Robotics: Science and Systems 2021 - [i74]Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg:
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos. CoRR abs/2101.07241 (2021) - [i73]Samarth Sinha, Animesh Garg:
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning. CoRR abs/2103.06326 (2021) - [i72]Mayank Mittal, David Hoeller, Farbod Farshidian, Marco Hutter, Animesh Garg:
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation. CoRR abs/2103.10534 (2021) - [i71]Arthur Allshire, Roberto Martín-Martín, Charles Lin, Shawn Manuel, Silvio Savarese, Animesh Garg:
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning. CoRR abs/2103.15793 (2021) - [i70]Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel van de Panne:
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model. CoRR abs/2104.09771 (2021) - [i69]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. CoRR abs/2105.04682 (2021) - [i68]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. CoRR abs/2105.06022 (2021) - [i67]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar:
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR abs/2105.08692 (2021) - [i66]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. CoRR abs/2105.12189 (2021) - [i65]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting. CoRR abs/2105.12244 (2021) - [i64]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. CoRR abs/2106.00136 (2021) - [i63]Michael Poli, Stefano Massaroli, Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg:
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions. CoRR abs/2106.04165 (2021) - [i62]Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels. CoRR abs/2106.14973 (2021) - [i61]Dylan P. Losey, Hong Jun Jeon, Mengxi Li, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Jeannette Bohg, Dorsa Sadigh:
Learning Latent Actions to Control Assistive Robots. CoRR abs/2107.02907 (2021) - [i60]Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, Yoav Artzi:
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution. CoRR abs/2107.05612 (2021) - [i59]Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. CoRR abs/2107.06434 (2021) - [i58]Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Ankur Handa, Animesh Garg:
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger. CoRR abs/2108.09779 (2021) - [i57]Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson:
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers. CoRR abs/2108.11996 (2021) - [i56]Jiankai Sun, De-An Huang, Bo Lu, Yun-Hui Liu, Bolei Zhou, Animesh Garg:
PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks. CoRR abs/2109.04869 (2021) - [i55]Stefan Bauer, Felix Widmaier, Manuel Wüthrich, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Charles B. Schaff, Takahiro Maeda, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Annika Buchholz, Sebastian Stark, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bernhard Schölkopf:
A Robot Cluster for Reproducible Research in Dexterous Manipulation. CoRR abs/2109.10957 (2021) - [i54]Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg:
Auditing AI models for Verified Deployment under Semantic Specifications. CoRR abs/2109.12456 (2021) - [i53]Haoping Xu, Yi Ru Wang, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects. CoRR abs/2110.00087 (2021) - [i52]Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters:
Continuous-Time Fitted Value Iteration for Robust Policies. CoRR abs/2110.01954 (2021) - [i51]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. CoRR abs/2110.10735 (2021) - [i50]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions. CoRR abs/2110.14538 (2021) - [i49]Matthew Shunshi Zhang, Murat A. Erdogdu, Animesh Garg:
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. CoRR abs/2111.00185 (2021) - [i48]Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara:
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. CoRR abs/2111.01365 (2021) - 2020
- [j6]Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese:
Learning task-oriented grasping for tool manipulation from simulated self-supervision. Int. J. Robotics Res. 39(2-3) (2020) - [j5]Vinu Joseph, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Animesh Garg:
A Programmable Approach to Neural Network Compression. IEEE Micro 40(5): 17-25 (2020) - [j4]Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks. IEEE Trans. Robotics 36(3): 582-596 (2020) - [c40]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Anima Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRL 2020: 883-894 - [c39]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c38]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. ICML 2020: 7360-7369 - [c37]Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh:
Controlling Assistive Robots with Learned Latent Actions. ICRA 2020: 378-384 - [c36]Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox:
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data. ICRA 2020: 4414-4420 - [c35]De-An Huang, Yu-Wei Chao, Chris Paxton, Xinke Deng, Li Fei-Fei, Juan Carlos Niebles, Animesh Garg, Dieter Fox:
Motion Reasoning for Goal-Based Imitation Learning. ICRA 2020: 4878-4884 - [c34]Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan D. Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox:
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning. ICRA 2020: 7505-7512 - [c33]Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, Marcus Dominguez-Kuhne, Animesh Garg, Roberto Martín-Martín, Silvio Savarese:
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter. IROS 2020: 8408-8414 - [c32]Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. NeurIPS 2020 - [c31]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. NeurIPS 2020 - [c30]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Smoothing. NeurIPS 2020 - [c29]Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. UAI 2020: 1378-1387 - [i47]Aysegul Dundar, Kevin J. Shih, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro:
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos. CoRR abs/2001.09518 (2020) - [i46]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Texture. CoRR abs/2003.01367 (2020) - [i45]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. CoRR abs/2003.03461 (2020) - [i44]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity inducing Information Bottleneck in Model Ensembles. CoRR abs/2003.04514 (2020) - [i43]Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan D. Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox:
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning. CoRR abs/2005.10872 (2020) - [i42]Homanga Bharadhwaj, Animesh Garg, Florian Shkurti:
Dynamics-Aware Latent Space Reachability for Exploration in Temporally-Extended Tasks. CoRR abs/2005.10934 (2020) - [i41]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. CoRR abs/2006.13169 (2020) - [i40]Samarth Sinha, Anirudh Goyal, Animesh Garg:
Maximum Entropy Models for Fast Adaptation. CoRR abs/2006.16524 (2020) - [i39]Homanga Bharadhwaj, Dylan Turpin, Animesh Garg, Ashton Anderson:
De-anonymization of authors through arXiv submissions during double-blind review. CoRR abs/2007.00177 (2020) - [i38]Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. CoRR abs/2007.00631 (2020) - [i37]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. CoRR abs/2007.02863 (2020) - [i36]Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, Marcus Dominguez-Kuhne, Animesh Garg, Roberto Martín-Martín, Silvio Savarese:
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter. CoRR abs/2008.06073 (2020) - [i35]Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. CoRR abs/2008.07087 (2020) - [i34]Tim D. Barfoot, Jessica Burgner-Kahrs, Eric D. Diller, Animesh Garg, Andrew A. Goldenberg, Jonathan Kelly, Xinyu Liu, Hani E. Naguib, Goldie Nejat, Angela P. Schoellig, Florian Shkurti, Hallie Siegel, Yu Sun, Steven L. Waslander:
Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors. CoRR abs/2009.08577 (2020) - [i33]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRR abs/2009.10019 (2020) - [i32]Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, Animesh Garg:
D2RL: Deep Dense Architectures in Reinforcement Learning. CoRR abs/2010.09163 (2020) - [i31]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. CoRR abs/2010.14497 (2020) - [i30]Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, Animesh Garg:
Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. CoRR abs/2011.02404 (2020) - [i29]Augustin Harter, Andrew Melnik, Gaurav Kumar, Dhruv Agarwal, Animesh Garg, Helge J. Ritter:
Solving Physics Puzzles by Reasoning about Paths. CoRR abs/2011.07357 (2020) - [i28]Wei Yu, Wenxin Chen, Steve Easterbrook, Animesh Garg:
Action Concept Grounding Network for Semantically-Consistent Video Generation. CoRR abs/2011.11201 (2020) - [i27]Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg:
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. CoRR abs/2011.12363 (2020) - [i26]Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti:
Skill Transfer via Partially Amortized Hierarchical Planning. CoRR abs/2011.13897 (2020) - [i25]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. CoRR abs/2012.12209 (2020)
2010 – 2019
- 2019
- [j3]Sanjay Krishnan, Animesh Garg, Richard Liaw, Brijen Thananjeyan, Lauren Miller, Florian T. Pokorny, Ken Goldberg:
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards. Int. J. Robotics Res. 38(2-3) (2019) - [c28]Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation. CoRL 2019: 42-52 - [c27]Andrey Kurenkov, Ajay Mandlekar, Roberto Martin Martin, Silvio Savarese, Animesh Garg:
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers. CoRL 2019: 717-734 - [c26]De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles:
Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration. CVPR 2019: 8565-8574 - [c25]Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín, Animesh Garg, Silvio Savarese, Ken Goldberg:
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter. ICRA 2019: 1614-1621 - [c24]Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks. ICRA 2019: 8943-8950 - [c23]Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, Animesh Garg:
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks. IROS 2019: 1010-1017 - [c22]Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity. IROS 2019: 1048-1055 - [c21]De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles:
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning. IROS 2019: 2635-2642 - [i24]Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín, Animesh Garg, Silvio Savarese, Ken Goldberg:
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter. CoRR abs/1903.01588 (2019) - [i23]Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, Animesh Garg:
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks. CoRR abs/1906.08880 (2019) - [i22]Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks. CoRR abs/1907.13098 (2019) - [i21]De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles:
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning. CoRR abs/1908.06769 (2019) - [i20]Kevin J. Shih, Aysegul Dundar, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro:
Video Interpolation and Prediction with Unsupervised Landmarks. CoRR abs/1909.02749 (2019) - [i19]Andrey Kurenkov, Ajay Mandlekar, Roberto Martin Martin, Silvio Savarese, Animesh Garg:
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers. CoRR abs/1909.04121 (2019) - [i18]Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh:
Controlling Assistive Robots with Learned Latent Actions. CoRR abs/1909.09674 (2019) - [i17]Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation. CoRR abs/1910.13395 (2019) - [i16]Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan:
A Programmable Approach to Model Compression. CoRR abs/1911.02497 (2019) - [i15]Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity. CoRR abs/1911.04052 (2019) - [i14]Ajay Mandlekar, Fabio Ramos, Byron Boots, Li Fei-Fei, Animesh Garg, Dieter Fox:
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data. CoRR abs/1911.05321 (2019) - [i13]De-An Huang, Yu-Wei Chao, Chris Paxton, Xinke Deng, Li Fei-Fei, Juan Carlos Niebles, Animesh Garg, Dieter Fox:
Motion Reasoning for Goal-Based Imitation Learning. CoRR abs/1911.05864 (2019) - [i12]Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar:
Angular Visual Hardness. CoRR abs/1912.02279 (2019) - [i11]Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar:
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR abs/1912.03978 (2019) - 2018
- [c20]Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, Li Fei-Fei:
ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation. CoRL 2018: 879-893 - [c19]De-An Huang, Shyamal Buch, Lucio M. Dery, Animesh Garg, Li Fei-Fei, Juan Carlos Niebles:
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos. CVPR 2018: 5948-5957 - [c18]Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese:
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks. ICRA 2018: 1-8 - [c17]Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese:
Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision. Robotics: Science and Systems 2018 - [c16]Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher B. Choy, Silvio Savarese:
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image. WACV 2018: 858-866 - [i10]Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese:
Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision. CoRR abs/1806.09266 (2018) - [i9]De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles:
Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration. CoRR abs/1807.03480 (2018) - [i8]Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks. CoRR abs/1810.10191 (2018) - [i7]Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, Li Fei-Fei:
RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation. CoRR abs/1811.02790 (2018) - 2017
- [j2]Sanjay Krishnan, Animesh Garg, Sachin Patil, Colin Lea, Gregory D. Hager, Pieter Abbeel, Ken Goldberg:
Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning. Int. J. Robotics Res. 36(13-14): 1595-1618 (2017) - [c15]JunYoung Gwak, Christopher B. Choy, Manmohan Chandraker, Animesh Garg, Silvio Savarese:
Weakly Supervised 3D Reconstruction with Adversarial Constraint. 3DV 2017: 263-272 - [c14]Brijen Thananjeyan, Animesh Garg, Sanjay Krishnan, Carolyn Chen, Lauren Miller, Ken Goldberg:
Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning. ICRA 2017: 2371-2378 - [c13]Ajay Mandlekar, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese:
Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations. IROS 2017: 3932-3939 - [c12]James Harrison, Animesh Garg, Boris Ivanovic, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Marco Pavone:
AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems. ISRR 2017: 437-453 - [i6]JunYoung Gwak, Christopher B. Choy, Animesh Garg, Manmohan Chandraker, Silvio Savarese:
Weakly Supervised Generative Adversarial Networks for 3D Reconstruction. CoRR abs/1705.10904 (2017) - [i5]James Harrison, Animesh Garg, Boris Ivanovic, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Marco Pavone:
ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems. CoRR abs/1707.04674 (2017) - [i4]Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher B. Choy, Silvio Savarese:
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image. CoRR abs/1708.04672 (2017) - [i3]Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese:
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks. CoRR abs/1710.01813 (2017) - [i2]Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg:
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning. CoRR abs/1711.01503 (2017) - 2016
- [b1]Animesh Garg:
Optimization and Design for Automation of Brachytherapy Delivery and Learning Robot-Assisted Surgical Sub-Tasks. University of California, Berkeley, USA, 2016 - [c11]Animesh Garg, Siddarth Sen, Rishi Kapadia, Yiming Jen, Stephen McKinley, Lauren Miller, Ken Goldberg:
Tumor localization using automated palpation with Gaussian Process Adaptive Sampling. CASE 2016: 194-200 - [c10]Stephen McKinley, Animesh Garg, Siddarth Sen, David V. Gealy, Jonathan P. McKinley, Yiming Jen, Menglong Guo, Walter Doug Boyd, Ken Goldberg:
An interchangeable surgical instrument system with application to supervised automation of multilateral tumor resection. CASE 2016: 821-826 - [c9]Adithyavairavan Murali, Animesh Garg, Sanjay Krishnan, Florian T. Pokorny, Pieter Abbeel, Trevor Darrell, Ken Goldberg:
TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning. ICRA 2016: 4150-4157 - [c8]Siddarth Sen, Animesh Garg, David V. Gealy, Stephen McKinley, Yiming Jen, Ken Goldberg:
Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization. ICRA 2016: 4178-4185 - [c7]Sanjay Krishnan, Animesh Garg, Richard Liaw, Brijen Thananjeyan, Lauren Miller, Florian T. Pokorny, Ken Goldberg:
SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards. WAFR 2016: 672-687 - [i1]Sanjay Krishnan, Animesh Garg, Richard Liaw, Lauren Miller, Florian T. Pokorny, Ken Goldberg:
HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards. CoRR abs/1604.06508 (2016) - 2015
- [c6]Stephen McKinley, Animesh Garg, Siddarth Sen, Rishi Kapadia, Adithyavairavan Murali, Kirk A. Nichols, Susan Lim, Sachin Patil, Pieter Abbeel, Allison M. Okamura, Ken Goldberg:
A single-use haptic palpation probe for locating subcutaneous blood vessels in robot-assisted minimally invasive surgery. CASE 2015: 1151-1158 - [c5]Adithyavairavan Murali, Siddarth Sen, Ben Kehoe, Animesh Garg, Seth McFarland, Sachin Patil, W. Douglas Boyd, Susan Lim, Pieter Abbeel, Kenneth Y. Goldberg:
Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms. ICRA 2015: 1202-1209 - [c4]Sanjay Krishnan, Animesh Garg, Sachin Patil, Colin Lea, Gregory D. Hager, Pieter Abbeel, Ken Goldberg:
Transition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning. ISRR (2) 2015: 91-110 - 2014
- [c3]Animesh Garg, Timmy Siauw, Guang Yang, Sachin Patil, J. Adam M. Cunha, I-Chow Hsu, Jean Pouliot, Alper Atamtürk, Kenneth Y. Goldberg:
Exact reachability analysis for planning skew-line needle arrangements for automated brachytherapy. CASE 2014: 524-531 - 2013
- [j1]Animesh Garg, Timmy Siauw, Dmitry Berenson, J. Adam M. Cunha, I-Chow Hsu, Jean Pouliot, Dan Stoianovici, Ken Goldberg:
Robot-Guided Open-Loop Insertion of Skew-Line Needle Arrangements for High Dose Rate Brachytherapy. IEEE Trans Autom. Sci. Eng. 10(4): 948-956 (2013) - [c2]Animesh Garg, Sachin Patil, Timmy Siauw, J. Adam M. Cunha, I-Chow Hsu, Pieter Abbeel, Jean Pouliot, Ken Goldberg:
An algorithm for computing customized 3D printed implants with curvature constrained channels for enhancing intracavitary brachytherapy radiation delivery. CASE 2013: 466-473 - 2012
- [c1]Animesh Garg, Timmy Siauw, Dmitry Berenson, J. Adam M. Cunha, I-Chow Hsu, Jean Pouliot, Dan Stoianovici, Ken Goldberg:
Initial experiments toward automated robotic implantation of skew-line needle arrangements for HDR brachytherapy. CASE 2012: 26-33
Coauthor Index
Anima Anandkumar
aka: Animashree Anandkumar
aka: Animashree Anandkumar
Kenneth Y. Goldberg
aka: Ken Goldberg
aka: Ken Goldberg
Roberto Martin Martin
aka: Roberto Martín-Martín
aka: Roberto Martín-Martín
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