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Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

, , , , , and . 6th Annual Conference on Robot Learning, (2023)

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DEP-RL : Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems, , , , and . Preprint, (2022)DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems, , , , and . Proceedings of the Eleventh InternationalConference on Learning Representations (ICLR), (2023)Learning with Muscles : Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks, , , , , and . Proceedings of The 6th Conference on Robot Learning, 205, page 1178-1188. MLResearchPress, (2022)Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots., , and . CoRR, (2019)The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes., , , , , , and . J. Open Source Softw., 6 (67): 2752 (2021)Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks, , , , , and . 6th Annual Conference on Robot Learning, (2022)Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks, , , , , and . 6th Annual Conference on Robot Learning, (2023)A lightweight robotic arm with pneumatic muscles for robot learning., , and . ICRA, page 4086-4092. IEEE, (2016)Control of Musculoskeletal Systems Using Learned Dynamics Models., , , and . IEEE Robotics and Automation Letters, 3 (4): 3161-3168 (2018)Jointly learning trajectory generation and hitting point prediction in robot table tennis., , , , and . Humanoids, page 650-655. IEEE, (2016)