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DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation

Lu, Zhenyu; Wang, Ning; Shi, Donghao

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Authors

Zhenyu Lu

Donghao Shi



Abstract

Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations (LfD) is a highly effective paradigm for robotic learning, where a robot learns from human actions directly and can be used autonomously for new tasks, avoiding the complicated analytical calculation for motion programming. However, the learned skills are not easy to generalize to new cases where special constraints such as varying relative distance limitation of robotic end effectors for human-like cooperative manipulations exist. In this paper, we propose a dynamic movement primitives (DMPs) based skills learning framework for redundant dual-arm robots. The method, with a coupling acceleration term to the DMPs function, is inspired by the transient performance control of Barrier Lyapunov Functions (BLFs). The additional coupling acceleration term is calculated based on the constant joint distance and varying relative distance limitations of end effectors for object approaching actions. In addition, we integrate the generated actions in joint space and the solution for a redundant dual-arm robot to complete a human-like manipulation. Simulations undertaken in Matlab and Gazebo environments certify the effectiveness of the proposed method.

Citation

Lu, Z., Wang, N., & Shi, D. (in press). DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation. Complex and Intelligent Systems,

Journal Article Type Article
Acceptance Date Mar 31, 2021
Deposit Date Apr 28, 2021
Publicly Available Date Apr 29, 2021
Print ISSN 2199-4536
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Public URL https://uwe-repository.worktribe.com/output/7316944

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