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ORCIDAravind Rajeswaran
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2020 – today
- 2024
[j4]Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran:
Policy Architectures for Compositional Generalization in Control. RLJ 5: 2264-2283 (2024)- [j3]Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel:
Semi-Supervised One Shot Imitation Learning. RLJ 5: 2284-2297 (2024) - [c36]Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul McVay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent-Pierre Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Alexander Sax, Aravind Rajeswaran:
OpenEQA: Embodied Question Answering in the Era of Foundation Models. CVPR 2024: 16488-16498 - [c35]Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar:
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. ICRA 2024: 7530-7537 - [c34]Sneha Silwal, Karmesh Yadav, Tingfan Wu, Jay Vakil, Arjun Majumdar, Sergio Arnaud, Claire Chen, Vincent-Pierre Berges, Dhruv Batra, Aravind Rajeswaran, Mrinal Kalakrishnan, Franziska Meier, Oleksandr Maksymets:
What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments? ICRA 2024: 17515-17521 - [c33]Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel:
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control. IROS 2024: 8539-8546 - [i39]Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel:
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control. CoRR abs/2405.04798 (2024) - [i38]Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel:
Semi-Supervised One-Shot Imitation Learning. CoRR abs/2408.05285 (2024) - 2023
- [c32]Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran:
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. ICLR 2023 - [c31]Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang:
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. ICML 2023: 12511-12526 - [c30]Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran:
Masked Trajectory Models for Prediction, Representation, and Control. ICML 2023: 37607-37623 - [c29]Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar:
Real World Offline Reinforcement Learning with Realistic Data Source. ICRA 2023: 7176-7183 - [c28]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn
, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. ICRA 2023: 9197-9203 - [c27]Vikash Kumar, Rutav M. Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran:
RoboHive: A Unified Framework for Robot Learning. NeurIPS 2023 - [c26]Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier:
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? NeurIPS 2023 - [i37]Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier:
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? CoRR abs/2303.18240 (2023) - [i36]Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran:
Masked Trajectory Models for Prediction, Representation, and Control. CoRR abs/2305.02968 (2023) - [i35]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. CoRR abs/2306.00942 (2023) - [i34]Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar:
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. CoRR abs/2309.14236 (2023) - [i33]Sneha Silwal, Karmesh Yadav, Tingfan Wu, Jay Vakil, Arjun Majumdar, Sergio Arnaud, Claire Chen, Vincent-Pierre Berges, Dhruv Batra, Aravind Rajeswaran, Mrinal Kalakrishnan, Franziska Meier, Oleksandr Maksymets:
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments? CoRR abs/2310.02219 (2023) - [i32]Vikash Kumar, Rutav M. Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Jay Vakil, Abhishek Gupta, Aravind Rajeswaran:
RoboHive: A Unified Framework for Robot Learning. CoRR abs/2310.06828 (2023) - 2022
- [c25]Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta:
R3M: A Universal Visual Representation for Robot Manipulation. CoRL 2022: 892-909 - [c24]Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta:
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. ICML 2022: 17359-17371 - [c23]Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh:
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots. ICML 2022: 19626-19644 - [c22]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? L4DC 2022: 893-905 - [c21]Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. NeurIPS 2022 - [i31]Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery. CoRR abs/2202.00161 (2022) - [i30]Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta:
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. CoRR abs/2203.03580 (2022) - [i29]Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran:
Policy Architectures for Compositional Generalization in Control. CoRR abs/2203.05960 (2022) - [i28]Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta:
R3M: A Universal Visual Representation for Robot Manipulation. CoRR abs/2203.12601 (2022) - [i27]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? CoRR abs/2204.11134 (2022) - [i26]Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar:
Real World Offline Reinforcement Learning with Realistic Data Source. CoRR abs/2210.06479 (2022) - [i25]Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran:
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. CoRR abs/2212.05698 (2022) - [i24]Zhao Mandi, Homanga Bharadhwaj, Vincent Moens, Shuran Song, Aravind Rajeswaran, Vikash Kumar:
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning. CoRR abs/2212.05711 (2022) - [i23]Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang:
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. CoRR abs/2212.05749 (2022) - 2021
- [b1]Aravind Rajeswaran:
Broad Generalization through Domain Transfer: Abstractions and Algorithms. University of Washington, USA, 2021 - [c20]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. L4DC 2021: 1154-1168 - [c19]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. NeurIPS 2021: 3016-3028 - [c18]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. NeurIPS 2021: 15084-15097 - [c17]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. NeurIPS 2021: 22171-22183 - [c16]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS 2021: 28954-28967 - [i22]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. CoRR abs/2101.01857 (2021) - [i21]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR abs/2102.08363 (2021) - [i20]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. CoRR abs/2106.01345 (2021) - [i19]Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin:
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL. CoRR abs/2106.09119 (2021) - [i18]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. CoRR abs/2107.08829 (2021) - 2020
- [c15]Aravind Rajeswaran, Igor Mordatch, Vikash Kumar:
A Game Theoretic Framework for Model Based Reinforcement Learning. ICML 2020: 7953-7963 - [c14]Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov:
Lyceum: An efficient and scalable ecosystem for robot learning. L4DC 2020: 793-803 - [c13]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS 2020 - [i17]Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov:
Lyceum: An efficient and scalable ecosystem for robot learning. CoRR abs/2001.07343 (2020) - [i16]Aravind Rajeswaran, Igor Mordatch, Vikash Kumar:
A Game Theoretic Framework for Model Based Reinforcement Learning. CoRR abs/2004.07804 (2020) - [i15]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL : Model-Based Offline Reinforcement Learning. CoRR abs/2005.05951 (2020) - [i14]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. CoRR abs/2012.11547 (2020)
2010 – 2019
- 2019
- [c12]Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch:
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. ICLR (Poster) 2019 - [c11]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. ICML 2019: 1920-1930 - [c10]Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov:
Learning Deep Visuomotor Policies for Dexterous Hand Manipulation. ICRA 2019: 3636-3643 - [c9]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 - [c8]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. NeurIPS 2019: 113-124 - [i13]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. CoRR abs/1902.08438 (2019) - [i12]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. CoRR abs/1909.04630 (2019) - 2018
- [j2]Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan:
A graph partitioning algorithm for leak detection in water distribution networks. Comput. Chem. Eng. 108: 11-23 (2018) - [j1]Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran:
Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data. IEEE Trans. Smart Grid 9(5): 5113-5122 (2018) - [c7]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. ICLR (Poster) 2018 - [c6]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. ICLR 2018 - [c5]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 - [c4]Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov:
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. SIMPAR 2018: 35-42 - [i11]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. CoRR abs/1803.07246 (2018) - [i10]Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov:
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. CoRR abs/1803.10371 (2018) - [i9]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) - [i8]Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch:
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. CoRR abs/1811.01848 (2018) - 2017
- [c3]Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. ICLR (Poster) 2017 - [c2]Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade:
Towards Generalization and Simplicity in Continuous Control. NIPS 2017: 6550-6561 - [i7]Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade:
Towards Generalization and Simplicity in Continuous Control. CoRR abs/1703.02660 (2017) - [i6]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) - [i5]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. CoRR abs/1711.09874 (2017) - 2016
- [c1]Satya Jayadev P., Aravind Rajeswaran, Nirav P. Bhatt, Ramkrishna Pasumarthy:
A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis. ACC 2016: 5026-5031 - [i4]Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan:
A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks. CoRR abs/1606.01754 (2016) - [i3]Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran:
Identifying Topology of Power Distribution Networks Based on Smart Meter Data. CoRR abs/1609.02678 (2016) - [i2]Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, Balaraman Ravindran:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. CoRR abs/1610.01283 (2016) - 2015
- [i1]Aravind Rajeswaran, Shankar Narasimhan:
A New Method for Reconstructing Network Topology from Flux Measurements. CoRR abs/1506.00438 (2015)
Coauthor Index
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