Human Variability in Human-Robot Locomotion

Understanding natural human behavior is essential for designing Effective and well-perceived automation in physical human-robot interaction (pHRI). While model-based control strategies are increasingly applied in assistive systems, most current approaches assume humans behave deterministically, which is contradicting evidence from neuroscience that highlights the stochastic nature of human motor control. This paper presents a user study with 21 participants performing goal-directed locomotion while physically pushing a smart wheelchair. By analyzing unconstrained human-only trials, we focus on characterizing human inherent variability in the context of physical coupling. Our results reveal structured patterns of task-relevant and task-irrelevant variability across repetitions, suggesting that variability is not random but systematically shaped by the task. These findings offer important insights for future shared control systems that aim to accommodate, rather than disregard, human movement variability.