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Motion capture and reinforcement learning of dynamically stable humanoid movement primitives.

, , , , , , and . ICRA, page 5284-5290. IEEE, (2013)

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Off-line path integral reinforcement learning using stochastic robot dynamics approximated by sparse pseudo-input Gaussian processes: Application to humanoid robot motor learning in the real environment., and . ICRA, page 1311-1316. IEEE, (2013)Efficient reuse of previous experiences in humanoid motor learning., , , , , and . Humanoids, page 554-559. IEEE, (2014)MOSAIC for Multiple-Reward Environments., , , and . Neural Computation, 24 (3): 577-606 (2012)The eMOSAIC model for humanoid robot control., , , and . Neural Networks, (2012)Brain-controlled exoskeleton robot for BMI rehabilitation., , , , , and . Humanoids, page 21-27. IEEE, (2012)Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning., , , , , and . IEEE Robot. Automat. Mag., 23 (1): 96-105 (2016)Trajectory-model-based reinforcement learning: Application to bimanual humanoid motor learning with a closed-chain constraint., and . Humanoids, page 429-434. IEEE, (2013)Phase-dependent trajectory optimization for CPG-based biped walking using path integral reinforcement learning., and . Humanoids, page 255-260. IEEE, (2011)Switching multiple LQG controllers based on Bellman's optimality principle: Using full-state feedback to control a humanoid robot., and . IROS, page 3185-3191. IEEE, (2011)BCI Control of Whole-Body Simulated Humanoid by Combining Motor Imagery Detection and Autonomous Motion Planning., , , , , and . ICONIP (1), volume 8226 of Lecture Notes in Computer Science, page 310-318. Springer, (2013)