Learning Push Recovery for a Bipedal Humanoid Robot with Dynamical Movement Primitives

Dingsheng Luo

Abstract:

Maintaining the balance is always a challenge
issue for bipedal humanoid robots in dealing with various
locomotive tasks to serve human society, especially when the
real environment the robot worked within exhibits to be very
complex. Unlike plenty of previous successful approaches on
humanoid balancing or push recovery, in this research, the
Dynamical Movement Primitives (DMP) is employed to model
several typical bio-inspired strategies. As humanoid balancing
or push recovery could be regarded as a problem of how a robot
to get back to its ongoing behavior when a break happens on
account of external force or uneven terrain etc., the DMP model
becomes an alternative ideal choice due to its promising nature
of attractor. Meanwhile, the DMP composed of a set of differential
equations provides a compact parameterized representation
in modelling a motion strategy, and thus leads to a strategy
model that is suitable to be fulfilled with machine learning
techniques. In this research, the learning process for those
bio-inspired strategies modeled with DMP are completed by
adopting the stochastic policy gradient reinforcement learning
and imitation learning separately. Furthermore, with Gaussian
Process(GP) regression, push recovery strategies are generalized
taking the advantages of the invariance properties of the DMP
model. As a consequence, an online adaptive push recovery
control strategy is finally achieved. Experimental results on both
simulated robot and a real bipedal humanoid robot PKU-HR5
demonstrate the presented approach is effective and promising.