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From dynamic movement primitives to associative skill memories., , , , , , и . Robotics Auton. Syst., 61 (4): 351-361 (2013)Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards., , , , , , , , , и . ICLR, OpenReview.net, (2020)Learning motion primitive goals for robust manipulation., , , , , и . IROS, стр. 325-331. IEEE, (2011)Collective robot reinforcement learning with distributed asynchronous guided policy search., , , , и . IROS, стр. 79-86. IEEE, (2017)Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation., , , , , , , , , и 2 other автор(ы). CoRR, (2023)Watch, Try, Learn: Meta-Learning from Demonstrations and Reward., , , , , , , , , и . CoRR, (2019)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)How to train your robot with deep reinforcement learning: lessons we have learned., , , , , и . Int. J. Robotics Res., (2021)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)Compliant quadruped locomotion over rough terrain., , , , и . IROS, стр. 814-820. IEEE, (2009)