Toward a virtual neuromuscular control for robust walking in bipedal robots

Abstract

Walking controllers for bipedal robots have not yet
reached human levels of robustness in locomotion. Imitating the
human motor control might be an alternative strategy for gen-
erating robust locomotion in robots. We seek to control bipedal
robots with a specific neuromuscular human walking model
proposed previously. Here, we present a virtual neuromuscular
controller, VNMC, that emulates this neuromuscular model to
generate desired motor torques for a bipedal robot. We test
the VNMC on a high-fidelity simulation of the ATRIAS bipedal
robot constrained to the sagittal plane. We optimize the control
parameters to tolerate maximum ground-height changes, which
resulted in ATRIAS walking on a terrain with up to ±7 cm
height changes. We further evaluate the robustness of the
optimized controller to external and internal disturbances. The
optimized VNMC adapts to 90% of random terrains with
ground-height changes up to ±2 cm. It endures 95% of ±30 Ns
horizontal pushes on the trunk, and 90% of 8 Ns backward and
4 Ns forward impulses on the swing foot throughout the gait
cycle. Furthermore, the VNMC is resilient to modeling errors
and sensor noise much larger than the equivalent uncertainties
in the real robot. The results suggest VNMC as a potential
alternative to generate robust locomotion in bipedal robots.