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Updated: Mar 3, 2026

Movement Retraining using Real-time Feedback of Performance
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Adaptive querying for reward learning from human feedback.

Yashwanthi Anand1, Nnamdi Nwagwu1, Kevin Sabbe1

  • 1Oregon State University, Corvallis, OR, United States.

Frontiers in Robotics and AI
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces adaptive feedback selection to train robots using multiple human feedback formats. This approach optimizes query states and feedback types for more efficient and user-aligned robot learning, enhancing safety and adaptability.

Keywords:
information gaininteractive imitation learninglearning from human feedbacklearning from multiple formatsrobot learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Learning from human feedback is crucial for robot adaptation and safety.
  • Current methods often rely on a single feedback format, limiting learning potential.
  • Optimizing both query states and feedback modalities can improve robot learning efficiency.

Purpose of the Study:

  • To develop and evaluate an adaptive feedback selection method for learning robot behaviors from human input.
  • To investigate the benefits of using multiple feedback formats and optimizing query states.
  • To enhance the sample efficiency and effectiveness of robot training through human feedback.

Main Methods:

  • An iterative, two-phase approach: critical state selection followed by information-gain-based feedback format selection.
  • Incorporation of feedback cost and probability into the feedback format selection process.
  • Experimental validation in simulation and with a physical robot.

Main Results:

  • Demonstrated sample efficiency in learning to avoid undesirable behaviors in simulation.
  • User studies confirmed the practicality and effectiveness of the adaptive feedback selection method.
  • The approach successfully accelerated robot learning by seeking informative, user-aligned feedback.

Conclusions:

  • Adaptive feedback selection significantly improves robot learning from human input by leveraging multiple feedback formats.
  • Optimizing query states and feedback modalities is key to efficient and effective human-robot learning.
  • This method offers a practical and effective solution for enhancing robot safety and user alignment.