All Publications and Preprints
Preprints
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin
ArXiv Preprint 2021 | arXiv: 2106.09119 | Project Website
Published in 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
Neural Information Processing Systems (NeurIPS) 2021 | Project Website
Visual Adversarial Imitation Learning using Variational Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Neural Information Processing Systems (NeurIPS) 2021 | Project Webpage
COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS) 2021 | arXiv: 2102.08363
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao,
Pieter Abbeel, Michael Laskin
Neural Information Processing Systems (NeurIPS) 2021
Offline Reinforcement Learning from Images with Latent Space Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Learning for Dynamics and Control (L4DC) 2021; (Oral Presentation!) Project Webpage
Published in 2020
MOReL : Model-Based Offline Reinforcement Learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
Neural Information Processing Systems (NeurIPS) 2020; Project Webpage
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
International Conference on Machine Learning (ICML) 2020; Project Webpage
Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, Emanuel Todorov
Learning for Dynamics and Control (L4DC) 2020; Project Webpage
Published in 2019
Meta Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine
Neural Information Processing Systems (NeurIPS) 2019; arXiv:1909.04630
Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
International Conference on Machine Learning (ICML) 2019; arXiv:1902.08438
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch
International Conference on Learning Representations (ICLR) 2019; arXiv:1811.01848
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low Cost
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA) 2019; arXiv:1810.06045
Learning Deep Visuomotor Policies for Dexterous Hand Manipulation
Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov
International Conference on Robotics and Automation (ICRA) 2019; download PDF
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Kendall Lowrey, Svetsolav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
IEEE SIMPAR 2018; arXiv:1803.10371 (Best paper award!)
Variance Reduction for Policy Gradient Using Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen,
Sham Kakade, Igor Mordatch, Pieter Abbeel
International Conference on Learning Representations (ICLR) 2018; arXiv:1803.07246
(Oral Presentation; top 2%)
Divide-and-Conquer Reinforcement Learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
International Conference on Learning Representations (ICLR) 2018; arXiv:1711.09874
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine
Robotics: Science and Systems (RSS) 2018; arxiv:1709.10087
Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade
Neural Information Processing Systems (NIPS) 2017; arXiv:1703.02660
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine
International Conference on Learning Representations (ICLR) 2017; arXiv:1610.01283
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
American Control Conference (ACC) 2016; arXiv:1511.06063
Identifying Topology of Power Distribution Networks Based on Smart Meter Data
Jayadev P Satya, Nirav Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran
IEEE Transactions on Smart Grid 2017; arXiv:1609.02678
A Graph Partitioning Approach for Leak Detection in Water Distribution Networks
Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan
Computers & Chemical Engineering (C&ChE) 2017; arXiv:1606.01754
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