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Summary: Standing Balance Control Using a Trajectory Library
Chenggang Liu and Christopher G. Atkeson
Abstract-- This paper presents a standing balance controller
that explicitly handles pushes. We employ a library of optimal
trajectories and the neighboring optimal control method to
generate local approximations to the optimal control. We take
advantage of a parametric nonlinear optimization method,
SNOPT, to generate initial trajectories and then use Differential
Dynamic Programming (DDP) to further refine them and get
their neighboring optimal control. A library generation method
is proposed, which keeps the trajectory library to a reasonable
size. We compare the proposed controller with an optimal
controller and an LQR based gain scheduling controller using
the same optimization criterion. Simulation results demonstrate
the performance of the proposed method.
I. INTRODUCTION
Humanoid robots are expected to interact with humans
and complex unstructured environments, so unexpected per-
turbations, such as collisions with people or moving objects,
are inevitable. This paper focuses on balance control during
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