This seminar will be held in person in Wu and Chen Auditorium as well as virtually via Zoom.
Multi-contact sliding mechanics in general and multilegged slipping in particular have long been considered difficult to model. As a consequence roboticists have avoided building multilegged systems and designing motion plans which include intentional slipping. I present a series of experiments and mathematical advances that demonstrate how these problems become easier with more contacts. These advances have allowed us to create fast learning algorithms that identify highly predictive models for the interaction physics of multi-contact gaits from a few dozen cycles of motion. The consequences are multifold: gait optimization algorithms for slipping and soft robots, speeding up simulations from linear to logarithmic dependence in the planning horizon, a deeper understanding of the relationship between Coulomb and Viscous friction, and perhaps some tantalizing hints as to the evolutionary origins of animals’ motor control. The work presented was funded by the NSF CMMI 1825918, ARO W911NF-14-1-0573, ARO W911NF-17-1-0306, and the D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project.