Aravind Rajeswaran

According to our database, Aravind Rajeswaran authored at least 49 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control.

Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel

CoRR, 2024

Policy Architectures for Compositional Generalization in Control.

Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran

RLJ, 2024

Semi-Supervised One Shot Imitation Learning.

Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel

RLJ, 2024

What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?

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

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation.

Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

OpenEQA: Embodied Question Answering in the Era of Foundation Models.

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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

RoboHive: A Unified Framework for Robot Learning.

Vikash Kumar, Rutav M. Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Jay Vakil, Abhishek Gupta, Aravind Rajeswaran

CoRR, 2023

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

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

CoRR, 2023

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

RoboHive: A Unified Framework for Robot Learning.

Vikash Kumar, Rutav M. Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Real World Offline Reinforcement Learning with Realistic Data Source.

Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Train Offline, Test Online: A Real Robot Learning Benchmark.

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

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Masked Trajectory Models for Prediction, Representation, and Control.

Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran

Proceedings of the International Conference on Machine Learning, 2023

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline.

Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang

Proceedings of the International Conference on Machine Learning, 2023

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations.

Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning.

Zhao Mandi, Homanga Bharadhwaj, Vincent Moens, Shuran Song, Aravind Rajeswaran, Vikash Kumar

CoRR, 2022

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery.

Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

CoRR, 2022

Unsupervised Reinforcement Learning with Contrastive Intrinsic Control.

Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?

Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran

Proceedings of the Learning for Dynamics and Control Conference, 2022

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots.

Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh

Proceedings of the International Conference on Machine Learning, 2022

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control.

Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta

Proceedings of the International Conference on Machine Learning, 2022

R3M: A Universal Visual Representation for Robot Manipulation.

Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta

Proceedings of the Conference on Robot Learning, 2022

2021

Broad Generalization through Domain Transfer: Abstractions and Algorithms.

Aravind Rajeswaran

PhD thesis, 2021

Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL.

Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin

CoRR, 2021

COMBO: Conservative Offline Model-Based Policy Optimization.

Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reinforcement Learning with Latent Flow.

Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Visual Adversarial Imitation Learning using Variational Models.

Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Decision Transformer: Reinforcement Learning via Sequence Modeling.

Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Offline Reinforcement Learning from Images with Latent Space Models.

Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

2020

MOReL: Model-Based Offline Reinforcement Learning.

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Lyceum: An efficient and scalable ecosystem for robot learning.

Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

A Game Theoretic Framework for Model Based Reinforcement Learning.

Aravind Rajeswaran, Igor Mordatch, Vikash Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Meta-Learning with Implicit Gradients.

Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost.

Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar

Proceedings of the International Conference on Robotics and Automation, 2019

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation.

Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov

Proceedings of the International Conference on Robotics and Automation, 2019

Online Meta-Learning.

Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine

Proceedings of the 36th International Conference on Machine Learning, 2019

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control.

Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data.

Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran

IEEE Trans. Smart Grid, 2018

A graph partitioning algorithm for leak detection in water distribution networks.

Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan

Comput. Chem. Eng., 2018

Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system.

Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov

Proceedings of the 2018 IEEE International Conference on Simulation, 2018

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine

Proceedings of the Robotics: Science and Systems XIV, 2018

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines.

Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel

Proceedings of the 6th International Conference on Learning Representations, 2018

Divide-and-Conquer Reinforcement Learning.

Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine

CoRR, 2017

Towards Generalization and Simplicity in Continuous Control.

Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles.

Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Identifying Topology of Power Distribution Networks Based on Smart Meter Data.

Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran

CoRR, 2016

A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis.

Satya Jayadev P., Aravind Rajeswaran, Nirav P. Bhatt, Ramkrishna Pasumarthy

Proceedings of the 2016 American Control Conference, 2016

2015

A New Method for Reconstructing Network Topology from Flux Measurements.

Aravind Rajeswaran, Shankar Narasimhan

CoRR, 2015