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

Yiwei Wang, Glenn J. Lematta, Chi Ping Hsiung, Kyleigh A. Rahm, Erin K. Chiou, Wenlong Zhang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article number060901
JournalJournal of Mechanisms and Robotics
Issue number6
StatePublished - Dec 2019
Externally publishedYes


  • dynamics
  • optimization
  • physical human–robot interaction
  • reliance
  • trust

ASJC Scopus subject areas

  • Mechanical Engineering


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