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VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training.

, , , , , and . ICLR, OpenReview.net, (2023)

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ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots., , , , , , and . CoRL, volume 100 of Proceedings of Machine Learning Research, page 1300-1313. PMLR, (2019)Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost., , , , and . CoRR, (2018)Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations., , , , , and . CoRR, (2017)RoboHive: A Unified Framework for Robot Learning., , , , , , , and . CoRR, (2023)Zero-Shot Robot Manipulation from Passive Human Videos., , , and . CoRR, (2023)Learning Deep Visuomotor Policies for Dexterous Hand Manipulation., , , , , and . ICRA, page 3636-3643. IEEE, (2019)Fast, strong and compliant pneumatic actuation for dexterous tendon-driven hands., , and . ICRA, page 1512-1519. IEEE, (2013)A semantic policy sharing and adaptation infrastructure for pervasive communities.. WWW (Companion Volume), page 155-160. ACM, (2012)Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations., , , , , , and . Robotics: Science and Systems, (2018)R3M: A Universal Visual Representation for Robot Manipulation., , , , and . CoRL, volume 205 of Proceedings of Machine Learning Research, page 892-909. PMLR, (2022)