Learning to control redundant musculoskeletal systems - arm and leg robot and computer simulation

Abstract:

Biological motion is fascinating in almost every aspect you look upon it. Especially locomotion plays a crucial part in the evolution of life. The fitness of species depends on movement speed, acceleration, maneuverability, endurance, etc. and decides the vital game between predator and prey and finally whether new offspring comes to life. Even nowadays, reduced movement capabilities, e.g. a broken femur following a fall, can have fatal consequences. Structures, like the bones connected by joints, soft and connective tissues and contracting proteins in a muscle-tendon unit enable and prescribe the respective species' specific locomotion patterns. Most importantly, biological motion is autonomously learned, it is untethered as there is no external energy supply like, for example, an air reservoir or electric plug, and typically in vertebrates, it's muscle-driven. This talk is focused on human motion. Digital models and biologically inspired robots are presented, built for a better understanding of biology’s complexity. Modeling musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. It is not only investigated whether machine learning approaches are capable of learning a controller for such systems. It is also of interest whether or not, the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. Experiments on a simulated musculoskeletal model of a human arm and leg and real biomimetic muscle-driven robots show that it is possible to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency. Thus, the basic understanding of the interplay between the enormous powerful human brain and the ingenious evolutionary design is enhanced. From an application point of view, this work is motivated by learning to control musculoskeletal systems.

Bio:

Syn Schmitt studied physics at the University of Stuttgart and graduated from the University of Tuebingen with a PhD in computational biophysics (topic: muscle modelling). In 2008, Schmitt was associated with the Stuttgart Research Centre for Simulation Technology (SimTech). He was appointed as Juniorprofessor (assistant professor) at the University of Stuttgart in 2012. In 2016, he received the fellowship of the Stuttgart Center for Simulation Science (SimTech). Since 2018, he is full professor of "Computational Biophysics and Biorobotics" at the University of Stuttgart and in 2019 he founded the Institute for Modelling and Simulation of Biomechanical Systems, together with his colleague Oliver Roehrle. Syn Schmitt is a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS), a member of the German Physicist Society (DPG) and the German Association of Applied Mathematics and Mechanics (GAMM). His research focusses on autonomous muscle-driven motion with special interests on design principles of the locomotion apparatus, non-linear dynamics of locomotion, motor control and morphological computation in biological and technical systems.