Autonomous muscle-driven motion in neuromechanics and biorobotics - learning to point, balance, walk, jump, reduce tremor, flex the spine and fall into the bathtub

Abstract: Biological motion is fascinating. Especially locomotion plays a crucial part in the evolution of life. Highly evolved structures, like bones connected by joints and soft tissues and contracting proteins in a muscle-tendon unit, enable and prescribe the respective species' specific locomotion pattern. Most importantly, biological motion is autonomously learned, it is untethered as there is no external energy supply and typical for vertebrates, it's muscle-driven. We approach this fascinating topic from a modelling and simulation perspective.

Bio: Syn has a PhD in theoretical physics. Since 2018, he is full professor of computational biophysics and biorobotics at the University of Stuttgart. In 2019 he founded the Institute for Modelling and Simulation of Biomechanical Systems. Syn is a fellow of the Stuttgart Center for Simulation Science and a faculty member of the International Max Planck Research School for Intelligent Systems. Since 2019, he is appointed as adjunct professor at QUT. His research focuses on autonomous muscle-driven motion especially design principles of the locomotion apparatus, non-linear dynamics of locomotion, learning motor control and morphological computation in biological and technical systems.

Selected Publications:

• Nadine Badie and Syn Schmitt. “Enhancing stance robustness and jump height in bipedal muscle-actuated systems: a bioinspired morphological development approach”. In: Bioinspiration & Biomimetics 19.3 (2024), p.036012. DOI: 10.1088/1748-3190/ad3602.
• Elsa K. Bunz, Daniel F. B. Haeufle, C. David Remy, and Syn Schmitt. “Bioinspired preactivation reflex increases robustness of walking on rough terrain”. In: Scientific Reports 13.1 (2023), p. 13219. DOI:10.1038/s41598-023-39364-3.
• Pierre Schumacher, Daniel F.B. Haeufle, Dieter Büchler, Syn Schmitt, and Georg Martius. “DEP-RL: Embodied Exploration for Reinforcement Learning in Overactu- ated and Musculoskeletal Systems”. In: Proceedings of the Eleventh International Conference on Learning Representations (ICLR). 2023. URL: https://openreview.net/forum?id=C-xa_D3oTj6.

Lab's webpage: https://biomechanicsbiorobotics.info/

Access to the meeting: Zoom Link