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Validation of Dynamic Bayesian Optimization for a Non-Stationary Human-in-the-Loop Optimization Problem.

GilHwan Kim¹, Fabrizio Sergi²

¹Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA.

IEEE Robotics and Automation Letters

|April 9, 2026

Summary

This summary is machine-generated.

Dynamic Bayesian Optimization (DBO) improved human-in-the-loop optimization for robot-assisted training by adapting to system changes. DBO outperformed traditional Bayesian Optimization in tracking optimal parameters during non-stationary conditions.

Keywords:

Human Factors and Human-in-the-Loop Machine Learning for Robot Control Wearable Robotics

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Area of Science:

Robotics
Biomechanics
Machine Learning

Background:

Human-in-the-Loop Optimization (HILO) is effective for assistive and augmentative tasks.
Conventional Bayesian Optimization (BO) struggles with non-stationary human-robot systems, limiting applications in training and rehabilitation.

Purpose of the Study:

To implement HILO using Dynamic Bayesian Optimization (DBO) for optimizing control parameters in robot-assisted training.
To compare DBO against BO in a non-stationary environment simulating robot-assisted rehabilitation.

Main Methods:

HILO was implemented using DBO and BO to determine optimal hip exoskeleton torques for maximizing hip extension angle during walking.
Sixteen participants walked on a treadmill with gradually increasing speed to induce non-stationarity.
The performance of DBO and BO was evaluated based on parameter convergence and accuracy.

Main Results:

DBO demonstrated superior performance compared to BO, especially in later training stages.
DBO led to a lower cost function and more accurate final torque values.
DBO effectively modeled the history-dependent input-output relationship, outperforming BO's handling of process noise.

Conclusions:

DBO is a more effective HILO strategy than BO for non-stationary human-robot systems.
DBO's ability to model system dynamics improves performance in robot-assisted training and rehabilitation.
This study highlights DBO's potential for adaptive and personalized robotic assistance.