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Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
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Robot Motor Skill Transfer With Alternate Learning in Two Spaces.

Jian Fu, Xiang Teng, Ce Cao

    IEEE Transactions on Neural Networks and Learning Systems
    |September 24, 2020
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    Summary

    This study introduces a new robot motion planning framework using imitation learning to generate novel robot movements for different tasks. The method enables robots to transfer and adapt motor skills from demonstrated tasks to new ones effectively.

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    Area of Science:

    • Robotics
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Reinforcement learning enhances robot movement skills through learning from demonstration (LfD).
    • A key challenge is automatically generating new robot motions for tasks dissimilar to demonstrations while maintaining performance.

    Purpose of the Study:

    • To propose a novel framework for intelligent robot trajectory planning.
    • To enable robots to generate new motions for tasks with different performance metrics based on demonstrated skills.

    Main Methods:

    • Utilizes improved locally weighted regression (iLWR) and policy improvement with path integral by dual perturbation (PI²-DP) for policy representation and imitation learning.
    • Employs alternate reward-guided weight searching and basis function's adaptive evolving in basis function and weight spaces.
    • Constructs a sequence of two-tuples to link demonstration and new tasks for motor skill transfer.

    Main Results:

    • Demonstrates the feasibility and effectiveness of the proposed framework.
    • Successfully applied to SCARA manipulator, a 10-DOF planar robot, and UR robot for via-points trajectory planning.
    • Shows gradual acquisition of motor skills from similar to dissimilar tasks with varying performance metrics.

    Conclusions:

    • The proposed framework effectively addresses the challenge of generating new robot motions for diverse tasks.
    • The method facilitates robust motor skill transfer and adaptation in robots.
    • This research advances intelligent trajectory planning for robots through advanced imitation learning techniques.