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Hierarchy of Motor Control01:18

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Related Experiment Video

Updated: Jun 21, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

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Skill acquisition framework in multi-robot precision assembly based on cooperative compliant control.

Xiaogang Song1, Peng Xu1, Wenfu Xu1

  • 1School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, PR China; Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Harbin Institute of Technology, Shenzhen 518055, PR China.

ISA Transactions
|October 18, 2024
PubMed
Summary

This study introduces a novel framework for multi-robot assembly, enhancing learning efficiency for complex, tightly coupled tasks. It enables high-precision robotic assembly despite disturbances and without prior knowledge.

Keywords:
Multi-robot cooperative assemblyMulti-robot cooperative manipulatorsReinforcement learningSkill acquisition

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Area of Science:

  • Robotics and Automation
  • Manufacturing Engineering
  • Artificial Intelligence

Background:

  • High-precision robotic assembly is crucial in manufacturing but challenged by disturbances.
  • Existing research primarily addresses single or weakly coupled multi-robot systems.
  • Tightly coupled multi-robot assemblies with significant uncertainties are underexplored.

Purpose of the Study:

  • To propose an efficient skill-acquisition framework for complex, tightly coupled multi-robot assembly tasks.
  • To enhance learning efficiency in robotic assembly systems.
  • To address challenges posed by uncertain disturbances in cooperative robotic assembly.

Main Methods:

  • Integration of a dual-loop coupled force-position control (DLCFPC) algorithm for simultaneous motion and force control.
  • Implementation of a parallel skill-learning algorithm to accelerate skill acquisition.
  • Inclusion of collision detection mechanisms within the framework.

Main Results:

  • The proposed framework enables multi-robot systems to achieve high-precision assembly.
  • Demonstrated robustness against various disturbances during cooperative tasks.
  • Successful execution of complex tasks like peg-in-hole assembly without prior knowledge.

Conclusions:

  • The developed framework significantly improves learning efficiency for challenging robotic assembly.
  • It provides a robust solution for tightly coupled multi-robot systems facing uncertainties.
  • This approach facilitates high-precision cooperative assembly in complex manufacturing scenarios.