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Related Experiment Video

Updated: Mar 3, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards.

Kathleen M Jagodnik, Philip S Thomas, Antonie J van den Bogert

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 6, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Human rewards effectively train Functional Electrical Stimulation (FES) controllers for spinal cord injury patients. Reinforcement learning (RL) controllers trained with human feedback showed efficient learning and outperformed standard methods.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Technology

    Background:

    • Functional Electrical Stimulation (FES) uses neuroprostheses to restore movement in individuals with spinal cord injury.
    • Current FES controllers struggle to adapt to individual physiological variations and user preferences.
    • Reinforcement learning (RL) offers a promising approach by incorporating human feedback to shape controller behavior.

    Purpose of the Study:

    • To investigate the efficacy of using human-assigned rewards to train RL-based FES controllers.
    • To compare the learning performance of RL controllers trained with human rewards versus algorithm-generated rewards.
    • To evaluate the impact of reward characteristics on controller learning success.

    Main Methods:

    • Ten neurologically intact participants provided subjective numerical rewards to train RL controllers.
    • A planar musculoskeletal human arm simulation was used to evaluate goal-oriented reaching tasks.
    • Controller performance was assessed based on target success, time to reach, and target overshoot.
    • Comparison was made between human-trained RL controllers and algorithm-trained RL controllers.

    Main Results:

    • Both human-trained and algorithm-trained RL controllers demonstrated efficient learning.
    • Both RL controller groups significantly outperformed standard controllers in reaching tasks.
    • No significant difference in learning efficiency was observed between human-trained and algorithm-trained controllers.
    • Reward positivity and consistency did not correlate with learning success.

    Conclusions:

    • Human rewards are effective for training RL-based FES controllers.
    • RL controllers trained with human feedback can restore voluntary movement in simulated paralyzed limbs.
    • This approach holds potential for personalized FES control strategies in spinal cord injury rehabilitation.