A novel human-robot skill transfer method for contact-rich manipulation task

Dong, Jiale; Si, Weiyong; Yang, Chenguang

Abstract

Purpose: The purpose of this paper is to enhance the robot’s ability to complete multi-step contact tasks in unknown or dynamic environments, as well as the generalization ability of the same task in different environments. Design/methodology/approach: This paper proposes a framework that combines learning from demonstration (LfD), behavior tree (BT) and broad learning system (BLS). First, the original dynamic motion primitive is modified to have a better generalization ability for representing motion primitives. Then, a BT based on tasks is constructed, which will select appropriate motion primitives according to the environment state and robot ontology state, and then the BLS will generate specific parameters of the motion primitives based on the state. The weights of the BLS can also be optimized after each successful execution. Findings: The authors carried out the tasks of cleaning the desktop and assembling the shaft hole on Baxter and Elite robots, respectively, and both tasks were successfully completed, which proved the effectiveness of the framework. Originality/value: This paper proposes a framework that combines LfD, BT and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

• Journal Article Type: Article
• Acceptance Date: May 15, 2023
• Online Publication Date: Jun 7, 2023
• Publication Date: Jun 23, 2023
• Deposit Date: Jun 27, 2023
• Publicly Available Date: Jun 8, 2024
• Journal: Robotic Intelligence and Automation
• Print ISSN: 2754-6969
• Electronic ISSN: 2754-6977
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Issue: 3
• DOI: https://doi.org/10.1108/RIA-01-2023-0002
• Keywords: Skill learning, Broad learning system, Behavior tree, Assembling, Dynamic motion primitives
• Public URL: https://uwe-repository.worktribe.com/output/10890457
• Publisher URL: https://www.emerald.com/insight/content/doi/10.1108/RIA-01-2023-0002/full/html