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Aravind Rajeswaran
Aravind Rajeswaran
Meta AI, UC Berkeley
Verified email at cs.washington.edu - Homepage
Title
Cited by
Cited by
Year
Decision transformer: Reinforcement learning via sequence modeling
L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ...
Advances in neural information processing systems 34, 15084-15097, 2021
11422021
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A Rajeswaran, V Kumar, A Gupta, G Vezzani, J Schulman, E Todorov, ...
Robotics: Science and Systems (RSS), 2018
9862018
Meta-Learning with Implicit Gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Advances in Neural Information Processing Systems (NeurIPS), 2019
7802019
MOReL: Model-Based Offline Reinforcement Learning
R Kidambi, A Rajeswaran, P Netrapalli, T Joachims
Advances in Neural Information Processing Systems (NeurIPS), 2020
6102020
Online Meta-Learning
C Finn, A Rajeswaran, S Kakade, S Levine
International Conference on Machine Learning (ICML), 2019
4672019
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
A Rajeswaran, S Ghotra, B Ravindran, S Levine
International Conference on Learning Representations (ICLR), 2017
3972017
Towards generalization and simplicity in continuous control
A Rajeswaran, K Lowrey, EV Todorov, SM Kakade
Advances in neural information processing systems 30, 2017
3262017
Combo: Conservative offline model-based policy optimization
T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn
Advances in neural information processing systems 34, 28954-28967, 2021
3182021
R3m: A universal visual representation for robot manipulation
S Nair, A Rajeswaran, V Kumar, C Finn, A Gupta
arXiv preprint arXiv:2203.12601, 2022
2962022
Identifying topology of low voltage distribution networks based on smart meter data
SJ Pappu, N Bhatt, R Pasumarthy, A Rajeswaran
IEEE Transactions on Smart Grid 9 (5), 5113-5122, 2017
2412017
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
K Lowrey, A Rajeswaran, S Kakade, E Todorov, I Mordatch
International Conference on Learning Representations (ICLR), 2019
2302019
Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
H Zhu, A Gupta, A Rajeswaran, S Levine, V Kumar
International Conference on Robotics and Automation (ICRA), 2019
2052019
Variance reduction for policy gradient with action-dependent factorized baselines
C Wu, A Rajeswaran, Y Duan, V Kumar, AM Bayen, S Kakade, I Mordatch, ...
International Conference on Learning Representations (ICLR), 2018
1672018
The unsurprising effectiveness of pre-trained vision models for control
S Parisi, A Rajeswaran, S Purushwalkam, A Gupta
international conference on machine learning, 17359-17371, 2022
1422022
A Game Theoretic Framework for Model Based Reinforcement Learning
A Rajeswaran, I Mordatch, V Kumar
International Conference on Machine Learning, 7953-7963, 2020
1292020
Divide-and-conquer reinforcement learning
D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine
International Conference on Learning Representations (ICLR), 2018
1212018
Offline reinforcement learning from images with latent space models
R Rafailov, T Yu, A Rajeswaran, C Finn
Learning for dynamics and control, 1154-1168, 2021
1112021
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
K Lowrey, S Kolev, J Dao, A Rajeswaran, E Todorov
2018 IEEE International Conference on Simulation, Modeling, and Programming …, 2018
782018
Where are we in the search for an artificial visual cortex for embodied intelligence?
A Majumdar, K Yadav, S Arnaud, J Ma, C Chen, S Silwal, A Jain, ...
Advances in Neural Information Processing Systems 36, 2024
592024
Can foundation models perform zero-shot task specification for robot manipulation?
Y Cui, S Niekum, A Gupta, V Kumar, A Rajeswaran
Learning for dynamics and control conference, 893-905, 2022
572022
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