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A Robot Skill Learning Framework Based on Compliant Movement Primitives

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Abstract

Collaborative robots are increasingly widely used in our lives, and at the same time, the skill learning ability of robots is becoming more and more important. For this reason, a robot skill learning framework based on compliant movement primitives is proposed in this paper. The framework consists of four modules: kinesthetic teaching, task learning, compliant movement primitive library, and task generalization. Specifically, the trajectories are collected from the kinematics of the robot, and the stiffness profiles are collected from the designed variable stiffness interface based on stiffness optimization; then the collected data is optimized, segmented, and learned to create the robot’s compliant movement primitive library; the primitives in the library are adjusted and combined to generate the robot’s desired trajectory and desired stiffness, which are then input into the dynamics-based variable impedance controller; thereafter the controller drives the robot to perform the desired compliant motion and complete various tasks. The framework covers the entire process of robot skill learning and application, and the proposed compliant movement primitives can simultaneously achieve the robot’s trajectory learning and interactive compliance learning. The experiment of the robot learning to press buttons was carried out on a universal 6-DOF collaborative robot. The experimental results prove the effectiveness and safety of the framework and show its application value.

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Availability of Data and Material

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

This work is supported in part by National Natural Science Foundation of China under Grant 91948301 and Grant 51721003.

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Authors and Affiliations

  1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, China

    Saixiong Dou, Juliang Xiao, Wei Zhao, Hang Yuan & Haitao Liu

Authors
  1. Saixiong Dou
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  2. Juliang Xiao
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  3. Wei Zhao
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  4. Hang Yuan
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  5. Haitao Liu
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Contributions

Conceptualization: Saixiong Dou and Juliang Xiao; Methodology: Saixiong Dou, Juliang Xiao and Haitao Liu; Formal analysis and investigation: Saixiong Dou, Wei Zhao and Hang Yuan; Writing- original draft preparation: Saixiong Dou; Writing- review and editing: Saixiong Dou, Hang Yuan and Juliang Xiao; Funding acquisition: Juliang Xiao and Haitao Liu.

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Correspondence to Juliang Xiao.

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Dou, S., Xiao, J., Zhao, W. et al. A Robot Skill Learning Framework Based on Compliant Movement Primitives. J Intell Robot Syst 104, 53 (2022). https://doi.org/10.1007/s10846-022-01605-4

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