Biped robot falling motion control with human-inspired active compliance

Protecting robot from broken of falling is always a challenge issue for a bipedal humanoid robot in dealing with various locomotion related tasks to serve human society, especially as the assigned tasks turns increasingly complicated and the corresponding real environment gets more and more complex. Unlike several previous successful approaches on humanoid falling control, in this study, a new approach is suggested in the light of how human do when a fall happens. The proposed approach takes a tripod-like falling controller followed by a human-inspired active compliance strategy using joints' active flexion and torque increment. Therefore, robot falling action covers both stages of a fall, i.e. before and after landing impact, so that to reduce the fall damage as far as possible. And the tripod like posture prevents accumulation of kinetic energy, while the active compliance absorbs the impact energy in a tender way. Meanwhile, considering the complexity of robot dynamics, other than taking expert experiences, the proposed human-inspired falling control strategy is parametrically modelled and optimized with policy gradient reinforcement learning. Experiments on both simulation and real robot PKU-HR5.1 are performed, and the results demonstrate this approach is effective and promising.