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Bioinspired pneumatic muscle spring units mimicking the human motion apparatus: benefits for passive motion range and joint stiffness variation in antagonistic setups.

S. Wolfen, J. Walter, M. Günther, D. Haeufle, and S. Schmitt. M2VIP, page 1-6. IEEE, (2018)

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Erich F B Waldhäusl

Angelica B F Ale

Marcel B F Uhr

Daniel B Wilhelm

Daniel B Leineweber

Other publications of authors with the same name

Energy management that generates terrain following versus apex-preserving hopping in man and machine.K. Kalveram, D. Haeufle, A. Seyfarth, and S. Grimmer. Biol. Cybern., 106 (1): 1-13 (2012)A geometry- and muscle-based control architecture for synthesising biological movement.J. Walter, M. Günther, D. Haeufle, and S. Schmitt. Biol. Cybern., 115 (1): 7-37 (2021)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.D. Haeufle, B. Schmortte, H. Geyer, R. Müller, and S. Schmitt. Frontiers Comput. Neurosci., (2018)Musculo-Skeletal Models as Tools to Quantify Embodiment.D. Haeufle, M. Günther, and S. Schmitt. ECAL, page 68. MIT Press, (2015)Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping.K. Ghazi-Zahedi, D. Haeufle, G. Montúfar, S. Schmitt, and N. Ay. CoRR, (2015)Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.D. Haeufle, I. Wochner, D. Holzmüller, D. Driess, M. Günther, and S. Schmitt. Frontiers Robotics AI, (2020)A Commentary on Towards autonomous artificial agents with an active self: Modeling sense of control in situated action.C. Hao, N. Russwinkel, D. Haeufle, and P. Beckerle. Cogn. Syst. Res., (June 2023)Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties.D. Drieß, H. Zimmermann, S. Wolfen, D. Suissa, D. Haeufle, D. Hennes, M. Toussaint, and S. Schmitt. ICRA, page 6461-6468. IEEE, (2018)Slack-based tunable damping leads to a trade-off between robustness and efficiency in legged locomotion.A. Mo, F. Izzi, E. Gönen, D. Haeufle, and A. Badri-Spröwitz. CoRR, (2022)Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks.I. Wochner, P. Schumacher, G. Martius, D. Büchler, S. Schmitt, and D. Haeufle. CoRL, volume 205 of Proceedings of Machine Learning Research, page 1178-1188. PMLR, (2022)

BibSonomy

Disambiguation of "Haeufle, Daniel F. B."