Involuntary Stabilization in Discrete-Event Physical Human-Robot Interaction

TL;DR

This paper investigates how involuntary human behaviors, specifically force reproduction biases, influence the stability of physical human-robot interactions, revealing that these biases can both stabilize and destabilize the interaction depending on the conditions.

Contribution

It introduces a mathematical bias model for involuntary human behavior and derives stability conditions, validated through behavioral experiments across different body parts.

Findings

Bias asymptotically stabilizes the implicit equilibrium point.

Bias destabilizes the equilibrium point near it.

Experimental verification across hand, wrist, and foot.

Abstract

Robots are used by humans not only as tools but also to interactively assist and cooperate with humans, thereby forming physical human-robot interactions. In these interactions, there is a risk that a feedback loop causes unstable force interaction, in which force escalation exposes a human to danger. Previous studies have analyzed the stability of voluntary interaction but have neglected involuntary behavior in the interaction. In contrast to the previous studies, this study considered the involuntary behavior: a human's force reproduction bias for discrete-event human-robot force interaction. We derived an asymptotic stability condition based on a mathematical bias model and found that the bias asymptotically stabilizes a human's implicit equilibrium point far from the implicit equilibrium point and destabilizes the point near the point. The bias model, convergence of the interaction toward the implicit equilibrium point, and divergence around the point were consistently verified via behavioral experiments under three kinds of interactions using three different body parts: a hand finger, wrist, and foot. Our results imply that humans implicitly secure a stable and close relationship between themselves and robots with their involuntary behavior.