QQ客服 官方微博 反馈留言

	Learning push recovery for a bipedal humanoid robot with Dynamical Movement Primitives
	Luo, Dingsheng ; Han, Xiaoqiang ; Ding, Yaoxiang ; Ma, Yang ; Liu, Zhan ; Wu, Xihong
	2015
英文摘要	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. ? 2015 IEEE.; EI; 1013-1019; 2015-December
语种	英语
出处	15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015
DOI标识	10.1109/HUMANOIDS.2015.7363478
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436407]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Luo, Dingsheng,Han, Xiaoqiang,Ding, Yaoxiang,et al. Learning push recovery for a bipedal humanoid robot with Dynamical Movement Primitives. 2015-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。