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Summary: An Empirical Exploration of Phase Resetting for Robust
Biped Locomotion with Dynamical Movement Primitives
Jun Nakanishi, Jun Morimoto, Gen Endo, Gordon Cheng, Stefan Schaal§, and Mitsuo Kawato
ATR Computational Neuroscience Laboratories, Kyoto, Japan
ICORP, Japan Science and Technology Agency, Kyoto, Japan
Sony Corporation, Tokyo, Japan
§ Dept. of Computer Science and Neuroscience, University of Southern California, Los Angeles, USA
{jun,xmorimo,gendo,gordon}@atr.jp, sschaal@usc.edu, kawato@atr.jp
Abstract-- We propose a framework for learning biped
locomotion using dynamical movement primitives based on
nonlinear oscillators. In our previous work, we suggested
dynamical movement primitives as a central pattern gener-
ator (CPG) to learn biped locomotion from demonstration.
We introduced an adaptation algorithm for the frequency of
the oscillators based on phase resetting at the instance of
heel strike and entrainment between the phase oscillator and
mechanical system using feedback from the environment.
In this paper, we empirically explore the role of phase re-
setting in the proposed algorithm for robust biped locomotion.
We demonstrate that phase resetting contributes to robustness
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