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Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation.

, , , , , , , , , , и . CoRL, том 87 из Proceedings of Machine Learning Research, стр. 651-673. PMLR, (2018)

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Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks., , , , , , , , и . CVPR, стр. 12627-12637. Computer Vision Foundation / IEEE, (2019)An Integrative Network Approach to Map the Transcriptome to the Phenome., , , , и . RECOMB, том 4955 из Lecture Notes in Computer Science, стр. 232-245. Springer, (2008)Fast, robust quadruped locomotion over challenging terrain., , , , и . ICRA, стр. 2665-2670. IEEE, (2010)Sequential operator splitting for constrained nonlinear optimal control., , и . ACC, стр. 4864-4871. IEEE, (2017)Combining planning techniques for manipulation using realtime perception., , и . ICRA, стр. 2895-2901. IEEE, (2010)Compliant quadruped locomotion over rough terrain., , , , и . IROS, стр. 814-820. IEEE, (2009)STOMP: Stochastic trajectory optimization for motion planning., , , , и . ICRA, стр. 4569-4574. IEEE, (2011)Skill learning and task outcome prediction for manipulation., , , , и . ICRA, стр. 3828-3834. IEEE, (2011)What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?, , , , , , , , , и 3 other автор(ы). CoRR, (2023)USA-Net: Unified Semantic and Affordance Representations for Robot Memory., , , , , и . IROS, стр. 1-8. (2023)