Deep Adversarial Imitation Learning of Locomotion Skills from One-shot Video Demonstration

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	Deep Adversarial Imitation Learning of Locomotion Skills from One-shot Video Demonstration
	Zhang HW(张会文)1,2,3; Liu YW(刘玉旺)2,3
	2019
会议日期	July 29 - August 2, 2019
会议地点	Suzhou, China
关键词	imitation learning GAN pose estimation locomotion skills
页码	1257-1261
英文摘要	Traditional imitation learning approaches usually collect demonstrations by teleoperation, kinesthetic teaching or precisely calibrated motion capture devices. These teaching interfaces are cumbersome and subject to the constraints of the environment and robot structures. Learning from observation adopts the idea that the robot can learn skills by observing human's behaviors, which is more convenient and preferable. However, learning from observation shows great challenges since it involves understanding of the environment and human actions, as well as solving the retarget problem. This paper presents a way to learn locomotion skills from a single video demonstration. We first leverage a weak supervised method to extract the pose feature from the experts, and then learn a joint position controller trying to match this feature by using the general adversarial network (GAN). This approach avoids cumbersome demonstrations, and more importantly, GAN can generalize learned skills to different subjects. We evaluated our method on a walking task executed by a 56 -degree-of-freedom (DOE) humanoid robot. The experiment demonstrate that the vision -based imitation learning algorithm can be applied to high -dimensional robot task and achieve comparable performance to methods by using finely calibrated motion capture data, which are of great significance for the research on human -robot interaction and robot skill acquisition.
产权排序	1
会议录	Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISSN号	2379-7711
ISBN号	978-1-7281-0770-7
WOS记录号	WOS:000569550300219
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/27670]
专题	沈阳自动化研究所_空间自动化技术研究室
通讯作者	Zhang HW(张会文)
作者单位	1.University of Chinese Academy of Sciences 2.Shenyang Institute of Automation, Chinese Academy of Sciences 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Zhang HW,Liu YW. Deep Adversarial Imitation Learning of Locomotion Skills from One-shot Video Demonstration[C]. 见:. Suzhou, China. July 29 - August 2, 2019.