Articles with public access mandates - Aravind RajeswaranLearn more
Available somewhere: 19
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
Mandates: US National Science Foundation
Meta-Learning with Implicit Gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Advances in Neural Information Processing Systems (NeurIPS), 2019
Mandates: US National Science Foundation, US Department of Defense
MOReL: Model-Based Offline Reinforcement Learning
R Kidambi, A Rajeswaran, P Netrapalli, T Joachims
Advances in Neural Information Processing Systems (NeurIPS), 2020
Mandates: US National Science Foundation
Online Meta-Learning
C Finn, A Rajeswaran, S Kakade, S Levine
International Conference on Machine Learning (ICML), 2019
Mandates: US National Science Foundation
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
Mandates: US Department of Defense
Towards generalization and simplicity in continuous control
A Rajeswaran, K Lowrey, EV Todorov, SM Kakade
Advances in neural information processing systems 30, 2017
Mandates: US National Science Foundation
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
Mandates: Department of Science & Technology, India
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
Mandates: US Department of Defense
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
Mandates: US National Science Foundation
A graph partitioning algorithm for leak detection in water distribution networks
A Rajeswaran, S Narasimhan, S Narasimhan
Computers & Chemical Engineering 108, 11-23, 2018
Mandates: Department of Science & Technology, India
A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis
SP Jayadev, A Rajeswaran, NP Bhatt, R Pasumarthy
2016 American Control Conference (ACC), 5026-5031, 2016
Mandates: Department of Science & Technology, India
Visual adversarial imitation learning using variational models
R Rafailov, T Yu, A Rajeswaran, C Finn
Advances in Neural Information Processing Systems 34, 3016-3028, 2021
Mandates: US Department of Defense
Masked trajectory models for prediction, representation, and control
P Wu, A Majumdar, K Stone, Y Lin, I Mordatch, P Abbeel, A Rajeswaran
International Conference on Machine Learning, 37607-37623, 2023
Mandates: US National Science Foundation, US Department of Defense
Real world offline reinforcement learning with realistic data source
G Zhou, L Ke, S Srinivasa, A Gupta, A Rajeswaran, V Kumar
2023 IEEE International Conference on Robotics and Automation (ICRA), 7176-7183, 2023
Mandates: US National Science Foundation, US Department of Defense
Unsupervised reinforcement learning with contrastive intrinsic control
M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel
Advances in Neural Information Processing Systems 35, 34478-34491, 2022
Mandates: US National Science Foundation, US Department of Defense
Lyceum: An efficient and scalable ecosystem for robot learning
C Summers, K Lowrey, A Rajeswaran, S Srinivasa, E Todorov
Learning for Dynamics and Control, 793-803, 2020
Mandates: US National Science Foundation, US Department of Defense, US National …
Train offline, test online: A real robot learning benchmark
G Zhou, V Dean, MK Srirama, A Rajeswaran, J Pari, K Hatch, A Jain, T Yu, ...
2023 IEEE International Conference on Robotics and Automation (ICRA), 9197-9203, 2023
Mandates: US National Science Foundation
Translating robot skills: Learning unsupervised skill correspondences across robots
T Shankar, Y Lin, A Rajeswaran, V Kumar, S Anderson, J Oh
International Conference on Machine Learning, 19626-19644, 2022
Mandates: US National Science Foundation
Contrastive intrinsic control for unsupervised reinforcement learning
M Laskin, H Liu, XB Peng, D Yarats, MAI NYU, A Rajeswaran, P Abbeel
Proceedings of the 36th International Conference on Neural Information …, 2022
Mandates: US National Science Foundation, US Department of Defense
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