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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Human-Inspired Force-Motion Imitation Learning with Dynamic Response for Adaptive Robotic Manipulation.

Yuchuang Tong¹, Haotian Liu¹, Tianbo Yang¹

¹CAS Engineering Laboratory for Intelligent Industrial Vision, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Biomimetics (Basel, Switzerland)

|December 24, 2025

Summary

This study introduces a bioinspired imitation learning framework for robots, enabling human-like adaptability and resilience in complex environments. The system efficiently learns and generalizes motion and force skills for natural robot interaction.

Keywords:

adaptive control bioinspired robotics force–motion skill acquisition imitation learning

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Area of Science:

Robotics
Artificial Intelligence
Bioinspired Engineering

Background:

Robots need adaptive control for dynamic environments.
Current strategies struggle with unpredictable disturbances.
Bioinspired approaches offer potential for natural robot interaction.

Purpose of the Study:

Develop a bioinspired imitation learning framework.
Enable robots to acquire and generalize motion and force skills.
Achieve compliant, resilient, and adaptive robot behavior.

Main Methods:

Integrated hybrid force-motion learning with dynamic response mechanisms.
Utilized dynamic movement primitives (DMPs) and a momentum-based force observer.
Employed a broad learning system (BLS) and adaptive RBFNN controller for skill refinement and parameter adjustment.

Main Results:

Achieved broad skill generalization without external sensors.
Demonstrated human-like adaptability, robustness, and scalability.
Reported efficient learning (5.56 s) and generation (0.036 s) times.

Conclusions:

The framework provides a lightweight, powerful solution for bioinspired intelligent control.
It enables efficient, real-time robot interaction in complex, unstructured environments.
The approach enhances robot safety and efficiency through adaptive dynamics.