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 generating 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.