Biped Robot Falling Motion Control with Human-inspired Active Compliance

Dingsheng Luo

Abstract:

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.