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Musculo-Skeletal Models as Tools to Quantify Embodiment., , and . ECAL, page 68. MIT Press, (2015)The Benefit of Combining Neuronal Feedback and Feed-Forward Control for Robustness in Step Down Perturbations of Simulated Human Walking Depends on the Muscle Function., , , , and . Frontiers Comput. Neurosci., (2018)Energy management that generates terrain following versus apex-preserving hopping in man and machine., , , and . Biol. Cybern., 106 (1): 1-13 (2012)A geometry- and muscle-based control architecture for synthesising biological movement., , , and . Biol. Cybern., 115 (1): 7-37 (2021)Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping., , , , and . CoRR, (2015)Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking., , , , , and . Frontiers Robotics AI, (2020)A Commentary on Towards autonomous artificial agents with an active self: Modeling sense of control in situated action., , , and . Cogn. Syst. Res., (June 2023)Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties., , , , , , , and . ICRA, page 6461-6468. IEEE, (2018)Slack-based tunable damping leads to a trade-off between robustness and efficiency in legged locomotion., , , , and . CoRR, (2022)Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks., , , , , and . CoRL, volume 205 of Proceedings of Machine Learning Research, page 1178-1188. PMLR, (2022)