Quantitative Modeling and Analysis of Reliance in Physical Human–Machine Coordination

Abstract

Abstract Smooth and efficient human–machine coordination in joint physical tasks may be realized through greater sensing and prediction of a human partner's intention to apply force to an object. In this paper, we define compliance and reliance in the context of physical human–machine coordination (pHMC) to characterize human responses in a joint object transport task. We apply an optimization framework to explain human intention and behavior. The weighting factor in the optimization problem, lambda (λ), is presented as a person's reliance on the machine in a joint physical task with varying constraints. We demonstrate that with an estimated λ, the intended two-dimensional motion of a person's trajectory can be captured. We also found a relationship between λ and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination. The extent to which λ may serve as an online measure of trust and reliance in a physical load sharing task requires further investigation under more complex task scenarios that involve greater degrees of vulnerability and uncertainty.