Aravind Rajeswaran

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 74

Top Topics

Reinforcement Learning

Visual Representation

Distribution Network

Top Venues

Publications

Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control. CoRR (2024)
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)
Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. ICRA (2024)
Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. ICLR (2023)
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 (2023)
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 (2023)
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)
Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar
Real World Offline Reinforcement Learning with Realistic Data Source. ICRA (2023)
Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. CoRR (2023)
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 (2023)
Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran
Masked Trajectory Models for Prediction, Representation, and Control. CoRR (2023)
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)
Vikash Kumar, Rutav M. Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran
RoboHive: A Unified Framework for Robot Learning. NeurIPS (2023)
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)
Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran
Masked Trajectory Models for Prediction, Representation, and Control. ICML (2023)
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 (2023)
Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran
Policy Architectures for Compositional Generalization in Control. CoRR (2022)
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? CoRR (2022)
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. CoRR (2022)
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? L4DC (2022)
Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar
Real World Offline Reinforcement Learning with Realistic Data Source. CoRR (2022)
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta
R3M: A Universal Visual Representation for Robot Manipulation. CoRL (2022)
Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. CoRR (2022)
Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. NeurIPS (2022)
Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots. ICML (2022)
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 (2022)
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 (2022)
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. ICML (2022)
Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery. CoRR (2022)
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta
R3M: A Universal Visual Representation for Robot Manipulation. CoRR (2022)
Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin
Reinforcement Learning with Latent Flow. NeurIPS (2021)
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Visual Adversarial Imitation Learning using Variational Models. CoRR (2021)
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR (2021)
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)
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Offline Reinforcement Learning from Images with Latent Space Models. L4DC (2021)
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Visual Adversarial Imitation Learning using Variational Models. NeurIPS (2021)
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 (2021)
Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL. CoRR (2021)
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS (2021)
Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin
Reinforcement Learning with Latent Flow. CoRR (2021)
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
A Game Theoretic Framework for Model Based Reinforcement Learning. CoRR (2020)
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov
Lyceum: An efficient and scalable ecosystem for robot learning. CoRR (2020)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS (2020)
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
A Game Theoretic Framework for Model Based Reinforcement Learning. ICML (2020)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL : Model-Based Offline Reinforcement Learning. CoRR (2020)
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Offline Reinforcement Learning from Images with Latent Space Models. CoRR (2020)
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov
Lyceum: An efficient and scalable ecosystem for robot learning. L4DC (2020)
Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine
Online Meta-Learning. ICML (2019)
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
Meta-Learning with Implicit Gradients. NeurIPS (2019)
Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov
Learning Deep Visuomotor Policies for Dexterous Hand Manipulation. ICRA (2019)
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
Meta-Learning with Implicit Gradients. CoRR (2019)
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)
Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine
Online Meta-Learning. CoRR (2019)
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. ICRA (2019)
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)
Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan
A graph partitioning algorithm for leak detection in water distribution networks. Comput. Chem. Eng. 108 (2018)
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
Divide-and-Conquer Reinforcement Learning. ICLR (Poster) (2018)
Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. CoRR (2018)
Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. CoRR (2018)
Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. SIMPAR (2018)
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 (2018)
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. CoRR (2018)
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) (2018)
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)
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade
Towards Generalization and Simplicity in Continuous Control. NIPS (2017)
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade
Towards Generalization and Simplicity in Continuous Control. CoRR (2017)
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. ICLR (Poster) (2017)
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR (2017)
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
Divide-and-Conquer Reinforcement Learning. CoRR (2017)
Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan
A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks. CoRR (2016)
Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, Balaraman Ravindran
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. CoRR (2016)
Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran
Identifying Topology of Power Distribution Networks Based on Smart Meter Data. CoRR (2016)
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)
Aravind Rajeswaran, Shankar Narasimhan
A New Method for Reconstructing Network Topology from Flux Measurements. CoRR (2015)