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Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states., , and . Neural Networks, (2017)Environment-adaptive interaction primitives for human-robot motor skill learning., , , , , and . Humanoids, page 711-717. IEEE, (2016)Kernel dynamic policy programming: Practical reinforcement learning for high-dimensional robots., , and . Humanoids, page 662-667. IEEE, (2016)Real-time stylistic prediction for whole-body human motions, , and . (2011)Learning CPG-based biped locomotion with a policy gradient method., , , , and . Humanoids, page 208-213. IEEE, (2005)Learning a dynamic policy by using policy gradient: application to biped walking., , , , and . Syst. Comput. Jpn., 38 (4): 25-38 (2007)Domains as Objectives: Multi-Domain Reinforcement Learning with Convex-Coverage Set Learning for Domain Uncertainty Awareness., , and . IROS, page 5622-5629. (2023)Deep Segmented DMP Networks for Learning Discontinuous Motions., , , and . CASE, page 1-7. IEEE, (2023)Multi-step motion learning by combining learning-from-demonstration and policy-search., , , and . Adv. Robotics, 37 (9): 560-575 (May 2023)Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation., , , , , and . CoRR, (2022)