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Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

Sep 16, 2021
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

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Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

Apr 28, 2021
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

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MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale

Apr 27, 2021
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman

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Visionary: Vision architecture discovery for robot learning

Mar 26, 2021
Iretiayo Akinola, Anelia Angelova, Yao Lu, Yevgen Chebotar, Dmitry Kalashnikov, Jacob Varley, Julian Ibarz, Michael S. Ryoo

* ICRA 2021 

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Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

Jun 29, 2020
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar, Zhe Su, Stefan Schaal, Gaurav S. Sukhatme, Franziska Meier

* Submitted to the International Journal of Robotics Research. Paper length is 21 pages (including references) with 12 figures. A video overview of the reinforcement learning experiment on the real robot can be seen at arXiv admin note: text overlap with arXiv:1710.08555 

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Meta-Learning via Learned Loss

Jun 12, 2019
Yevgen Chebotar, Artem Molchanov, Sarah Bechtle, Ludovic Righetti, Franziska Meier, Gaurav Sukhatme

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Learning Latent Space Dynamics for Tactile Servoing

Apr 15, 2019
Giovanni Sutanto, Nathan Ratliff, Balakumar Sundaralingam, Yevgen Chebotar, Zhe Su, Ankur Handa, Dieter Fox

* Accepted to be published at the International Conference on Robotics and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is 7 pages (i.e. 6 pages of technical content (including text, figures, tables, acknowledgement, etc.) and 1 page of the Bibliography/References), while this arXiv version is 8 pages (added Appendix and some extra details) 

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Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

Mar 05, 2019
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, Dieter Fox

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Path Integral Guided Policy Search

Oct 11, 2018
Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine

* Published at the International Conference on Robotics and Automation (ICRA), 2017 

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Time-Contrastive Networks: Self-Supervised Learning from Video

Mar 20, 2018
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine

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Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

Nov 23, 2017
Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph Lim

* Paper accepted to NIPS 2017 

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Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Jun 18, 2017
Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, Sergey Levine

* Paper accepted to the International Conference on Machine Learning (ICML) 2017 

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Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search

Oct 03, 2016
Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine

* Submitted to the IEEE International Conference on Robotics and Automation 2017 

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