Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives

Caiwei Song; Gangfeng Liu; Xuehe Zhang; Xizhe Zang; Congcong Xu; Jie Zhao

doi:10.1016/j.isatra.2019.08.007

Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives

ISA Trans. 2020 Feb:97:325-335. doi: 10.1016/j.isatra.2019.08.007. Epub 2019 Aug 5.

Authors

Caiwei Song¹, Gangfeng Liu², Xuehe Zhang², Xizhe Zang², Congcong Xu², Jie Zhao²

Affiliations

¹ School of Mechatronics Engineering, Harbin Institute of Technology (HIT), Harbin, 150001, China. Electronic address: cwsong@outlook.com.
² School of Mechatronics Engineering, Harbin Institute of Technology (HIT), Harbin, 150001, China.

PMID: 31395285
DOI: 10.1016/j.isatra.2019.08.007

Abstract

This paper presents a robot skill acquisition framework for learning and reproducing humanoid trajectories with complex forms. A new unsupervised segmentation method is proposed to detect motion units in the demonstrated kinematic data using the concept of key points. To find the consistent features of trajectories, a Hidden Semi-Markov Model (HSMM) is used to identify key points common to all the demonstrations. Generalizing the motion units is achieved via a Probability-based Movement Primitive (PbMP), which encapsulates multiple trajectories into one model. Such a framework can generate trajectories suitable for robot execution with arbitrary shape and complexity from a small number of demonstrations, which greatly expands the application scenarios of robot programming by demonstration. The automatic segmentation process does not rely on a priori knowledge or models for specific tasks, and the generalized trajectory retains more consistent features than those produced by other algorithms. We demonstrate the effectiveness of the proposed framework through simulations and experiments.

Keywords: Autonomous segmentation; Human–robot interaction; Motion and path planning; Robot learning through demonstration.