Lerrel Pinto
Cited by
Cited by
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L Pinto, A Gupta
2016 IEEE international conference on robotics and automation (ICRA), 3406-3413, 2016
Robust adversarial reinforcement learning
L Pinto, J Davidson, R Sukthankar, A Gupta
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Learning to fly by crashing
D Gandhi, L Pinto, A Gupta
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017
The curious robot: Learning visual representations via physical interactions
L Pinto, D Gandhi, Y Han, YL Park, A Gupta
European Conference on Computer Vision, 3-18, 2016
Asymmetric actor critic for image-based robot learning
L Pinto, M Andrychowicz, P Welinder, W Zaremba, P Abbeel
arXiv preprint arXiv:1710.06542, 2017
Learning to push by grasping: Using multiple tasks for effective learning
L Pinto, A Gupta
2017 IEEE International Conference on Robotics and Automation (ICRA), 2161-2168, 2017
Supervision via competition: Robot adversaries for learning tasks
L Pinto, J Davidson, A Gupta
2017 IEEE International Conference on Robotics and Automation (ICRA), 1601-1608, 2017
Improved learning of dynamics models for control
A Venkatraman, R Capobianco, L Pinto, M Hebert, D Nardi, JA Bagnell
International Symposium on Experimental Robotics, 703-713, 2016
Robot learning in homes: Improving generalization and reducing dataset bias
A Gupta, A Murali, DP Gandhi, L Pinto
Advances in Neural Information Processing Systems, 9094-9104, 2018
Predictive-state decoders: Encoding the future into recurrent networks
A Venkatraman, N Rhinehart, W Sun, L Pinto, M Hebert, B Boots, K Kitani, ...
Advances in Neural Information Processing Systems, 1172-1183, 2017
CASSL: Curriculum accelerated self-supervised learning
A Murali, L Pinto, D Gandhi, A Gupta
2018 IEEE International Conference on Robotics and Automation (ICRA), 6453-6460, 2018
Pyrobot: An open-source robotics framework for research and benchmarking
A Murali, T Chen, KV Alwala, D Gandhi, L Pinto, S Gupta, A Gupta
arXiv preprint arXiv:1906.08236, 2019
Multiple interactions made easy (mime): Large scale demonstrations data for imitation
P Sharma, L Mohan, L Pinto, A Gupta
arXiv preprint arXiv:1810.07121, 2018
Reinforcement Learning with Augmented Data
M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas
arXiv preprint arXiv:2004.14990, 2020
Environment probing interaction policies
W Zhou, L Pinto, A Gupta
arXiv preprint arXiv:1907.11740, 2019
Learning to manipulate deformable objects without demonstrations
Y Wu, W Yan, T Kurutach, L Pinto, P Abbeel
arXiv preprint arXiv:1910.13439, 2019
Sample-efficient learning of nonprehensile manipulation policies via physics-based informed state distributions
L Pinto, A Mandalika, B Hou, S Srinivasa
arXiv preprint arXiv:1810.10654, 2018
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
W Yan, A Vangipuram, P Abbeel, L Pinto
arXiv preprint arXiv:2003.05436, 2020
Automatic curriculum learning through value disagreement
Y Zhang, P Abbeel, L Pinto
arXiv preprint arXiv:2006.09641, 2020
Discovering Motor Programs by Recomposing Demonstrations
T Shankar, S Tulsiani, L Pinto, A Gupta
International Conference on Learning Representations, 2019
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