Model-based concepts for the control of assistive devices
We develop neuro-musculo-skeletal computer simulations of impaired motor control. These models can be used to estimate desing criteria for assistive devices. Furthermore, such models could be used to evaluate control strategies for assistive devices already in the design phase.
In collaboration with Syn Schmitt at Uni Stuttgart (https://www.imsb.uni-stuttgart.de/research/cbb/) and Christoph Keplinger at MPI-IS (https://rm.is.mpg.de/)
Selected publications: Stollenmaier, K., Rist, I. S., Izzi, F., & Haeufle, D. F. B. (2020). Simulating the response of a neuro-musculoskeletal model to assistive forces: implications for the design of wearables compensating for motor control deficits. *2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)*, 779–784. https://doi.org/10.1109/BioRob49111.2020.9224411
Compensating for expected step-down perturbations: modelling anticipatory control strategies in human walking
In preparation for a step-down, humans change the activation patterns of individual muscles already during the last ground contact before the step-down. By reducing muscle activity in the stance leg, they lower the height of their body's center of mass (Müller et al., 2020). In this project, we further investigate this anticipatory adaptation strategy with the help of a muscle reflex model and perturbation simulations.
Contributors: Lucas Schreff, in collaboration with Roy Müller at Klinikum Bayreuth (<https://www.researchgate.net/lab/GaitLab-Klinikum-Bayreuth-Roy-Mueller>)
Selected publications: Müller, R., Vielemeyer, J. & Häufle, D. F. B. (2020). Negotiating ground level perturbations in walking: Visual perception and expectation of curb height modulate muscle activity. Journal of Biomechanics, 113, 110121. doi.org/10.1016/j.jbiomech.2020.110121
Modelling motor control in rare neurodegenerative diseases
Motor control abnormalities are key symptoms in several rare neurodegenerative diseases. We contribute to a project in which our colleagues study the development and progression of movement dysfunctions by assessing kinematics of premanifest and manifest patients. Our role in this project is to develop neuro-mechanical models with the aim to develop a more detailed understanding of the progressive character of motor control dysfunction. Especially, we are interested in the relationship between altered neuromuscular reflexes and kinematics.
Contributors: Christian Laßmann, in Collaboration with Winfried Ilg and Ludger Schoels, HIH Tübingen
The contribution of muscles to the control of movement: quantifying morphological computation
Muscle fibres possess complex, nonlinear viscous properties, which allow to counter unexpected perturbations with zero time delay. This feature is critical during agile, legged locomotion, e.g., hopping and running, where sensory blindness due to neurotransmission delays can result in dangerous falls. By expanding the current understanding of the stabilising capacity of fibre viscosity, we aim to unveil new design principles for technical solutions in the field of legged robotics and medical rehabilitation/assistance.
Contributors: Fabio Izzi and Matthew Araz, In collaboration with MPI-IS (Link: dlg.is.mpg.de) and Uni Stuttgart (Link: www.inspo.uni-stuttgart.de/institut/av/)
Mo, A., Izzi, F., Haeufle, D. F. B., & Badri-Spröwitz, A. (2020). Effective Viscous Damping Enables Morphological Computation in Legged Locomotion. *Frontiers in Robotics and AI - Soft Robotics*, *7*(August), 1–15. doi.org/10.3389/frobt.2020.00110
Leveraging morphological computation in autonomous control
Muscle-driven biological systems effortlessly outperform even the most sophisticated robots in terms of adaptivity, longevity and complexity of behaviour. However, widespread adoption of biomimetic devices is partially hindered by the difficulty of precisely controlling muscular morphologies, for which classical control approaches tend to fail. We therefore aim to control these systems with novel reinforcement learning techniques, leveraging their ability to deal with complex nonlinear actuators. The combination of autonomously learning agents with biomechanically inspired morphologies may unlock some of the secrets encoded by millions of years of evolution, while dealing with the unique control difficulties that these morphologies exhibit.
Contributors: Pierre Schumacher, in Collaboration with Georg Martius at MPI-IS (<https://al.is.mpg.de/>) and Dieter Büchler at MPI-IS (https://www.is.mpg.de/person/dbuechler) and Syn Schmitt at Uni Stuttgart (Link: www.imsb.uni-stuttgart.de/research/cbb/).
Selected publications: Haeufle, D. F. B., Wochner, I., Holzmüller, D., Driess, D., Günther, M., & Schmitt, S. (2020). Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. *Frontiers in Robotics and AI*, *7*, Research topic: Recent Trends in Morphological Com. doi.org/10.3389/frobt.2020.00077
