Human-Variability-Respecting Optimal Control for Physical Human-Machine Interaction

TL;DR

This paper introduces a novel stochastic optimal control method that explicitly models human variability and noise, improving the accuracy and naturalness of physical human-machine interactions.

Contribution

It develops a new control framework incorporating stochastic human movement models, bridging the gap between neuroscientific standards and control design.

Findings

Enhanced accuracy in target reaching tasks

Reduced variability in system performance

Ability to preserve natural human movement variability

Abstract

Physical Human-Machine Interaction plays a pivotal role in facilitating collaboration across various domains. When designing appropriate model-based controllers to assist a human in the interaction, the accuracy of the human model is crucial for the resulting overall behavior of the coupled system. When looking at state-of-the-art control approaches, most methods rely on a deterministic model or no model at all of the human behavior. This poses a gap to the current neuroscientific standard regarding human movement modeling, which uses stochastic optimal control models that include signal-dependent noise processes and therefore describe the human behavior much more accurate than the deterministic counterparts. To close this gap by including these stochastic human models in the control design, we introduce a novel design methodology resulting in a Human-Variability-Respecting Optimal Control that explicitly incorporates the human noise processes and their influence on the mean and variability behavior of a physically coupled human-machine system. Our approach results in an improved overall system performance, i.e. higher accuracy and lower variability in target point reaching, while allowing to shape the joint variability, for example to preserve human natural variability patterns.