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Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction.

, , , , , , and . ICRA, page 9449-9456. IEEE, (2023)

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Anticipative Interaction Primitives for Human-Robot Collaboration., , , , and . AAAI Fall Symposia, AAAI Press, (2016)Learning inverse dynamics models in O(n) time with LSTM networks., , , and . Humanoids, page 811-816. IEEE, (2017)Comparative experiments on task space control with redundancy resolution., , , , and . IROS, page 3901-3908. IEEE, (2005)Learning robot grasping from 3-D images with Markov Random Fields., , and . IROS, page 1548-1553. IEEE, (2011)Towards compliant humanoids-an experimental assessment of suitable task space position/orientation controllers., , , and . IROS, page 2520-2527. IEEE, (2007)Regularizing Reinforcement Learning with State Abstraction., , , and . IROS, page 534-539. IEEE, (2018)Learning probabilistic discriminative models of grasp affordances under limited supervision., , , , , and . IROS, page 1586-1591. IEEE, (2010)Context-driven movement primitive adaptation., , and . ICRA, page 3469-3475. IEEE, (2017)Learning to predict phases of manipulation tasks as hidden states., , , and . ICRA, page 4009-4014. IEEE, (2014)Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement., , and . Auton. Agents Multi Agent Syst., 34 (2): 42 (2020)