NDLI: Learning push recovery for a bipedal humanoid robot with Dynamical Movement Primitives

Content Provider	IEEE Xplore Digital Library
Author	Dingsheng Luo Xiaoqiang Han Yaoxiang Ding Yang Ma Zhan Liu Xihong Wu
Copyright Year	2015
Description	Author affiliation: Key Lab. of Machine Perception, Peking Univ., Beijing, China (Dingsheng Luo; Xiaoqiang Han; Yaoxiang Ding; Yang Ma; Zhan Liu; Xihong Wu)
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.
Starting Page	1013
Ending Page	1019
File Size	963235
Page Count	7
File Format	PDF
e-ISBN	9781479968855
DOI	10.1109/HUMANOIDS.2015.7363478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-03
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot kinematics Biological system modeling Humanoid robots Robot sensing systems Mathematical model Hip
Content Type	Text
Resource Type	Article

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