Path Learning in Human-Robot Collaboration Tasks Using Iterative Learning Methods

• Published in: IEEE transactions on control systems technology Vol. 30; no. 5; pp. 1946 - 1959
• Main Authors: Xing, Xueyan, Xia, Jingkang, Huang, Deqing, Li, Yanan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Collaboration; Gradient estimation (GE); gradient search (GS); Human motion; human(–)robot collaboration (HRC); iterative learning; Iterative methods; Learning; Learning systems; Parameterization; Parameters; path learning; path planning; Performance analysis; Performance indices; Robots; Task analysis; Teaching methods; Trajectories; Trajectory; Uncertainty
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2021.3134070

In a repetitive human-robot collaboration (HRC) task, robots typically are required to learn the intended motion of the human user to improve the collaboration efficiency. However, the human user's trajectory is of uncertainty when repeating the same task (e.g., human hand tremor and uncertain movement speed), which may directly deteriorate the learning performance. To address this issue, a path characterized by spatial correlation parameters, is of necessity to be learned by robots so that the aforementioned time-related uncertainty will be avoided. In this article, based on the path parameterization, a gradient-based iterative path learning (IPL) strategy is designed for the robot to learn the desired path of human. The proposed IPL strategy draws on the iterative learning methods with a properly designed performance index. Since the gradient of the performance index is hard to directly obtain in HRC, two learning methods with gradient search (GS) and gradient estimation (GE) are developed. The GS estimates the gradient online and has an advantage of easy implementation. By contrast, the advantage of GS is more obvious when the number of learned parameters is considerable due to its high learning efficiency. With these two methods, the unknown path parameters can be iteratively updated toward the desired values. To verify the effectiveness of the proposed IPL algorithm, experiments are carried out. In the experiment, a comparison between GS and GE methods is made to display their respective advantages. Besides, the proposed IPL is compared with an existing trajectory learning method subject to two different kinds of uncertainties and its better learning performance verifies its stability and capability in dealing with uncertainty.