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Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting.

Xisheng Jiang1,2,3, Baolei Wu1, Simin Li1

  • 1School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Biomimetics (Basel, Switzerland)
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved motion retargeting algorithm for human-robot interaction (HRI), enhancing robot arm configuration similarity and end-effector accuracy. A novel multi-person pose estimation method enables real-time multi-robot collaboration.

Keywords:
human–robot interactionimproved retargetingmotion imitationmulti-person pose estimation

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Computer Vision

Background:

  • Human-robot interaction (HRI) relies on motion imitation for efficient collaboration.
  • Existing motion retargeting algorithms face limitations in balancing robot arm configuration similarity and end-effector tracking accuracy.
  • Differences in human and robot physical structures pose challenges for accurate motion transfer.

Purpose of the Study:

  • To develop an improved motion retargeting algorithm for humanoid robots.
  • To achieve both human-like robot arm configuration and precise end-effector position tracking.
  • To enable real-time multi-person motion capture for collaborative robot tasks.

Main Methods:

  • Proposed an improved retargeting algorithm combining joint space and Cartesian space considerations.
  • Introduced a multi-person pose estimation algorithm using a single RGB-D camera for real-time motion capture.
  • Integrated pose estimation with the retargeting algorithm for multi-robot collaboration.

Main Results:

  • The improved retargeting algorithm successfully ensured consistent robot arm configuration and accurate end-effector tracking.
  • The multi-person pose estimation algorithm demonstrated superior real-time performance.
  • Experimental results validated the algorithm's effectiveness in multi-robot collaborative scenarios.

Conclusions:

  • The proposed approach effectively addresses the limitations of traditional motion retargeting methods.
  • The integrated system enables efficient and accurate multi-robot collaboration through real-time motion imitation.
  • This work advances the capabilities of humanoid robots in complex HRI tasks.