Normalizing Task-Oriented Human-Robot Interaction for Large-Scale Virtual Environments

Physical simulation platforms, including haptic manipulanda and robotic rehabilitation systems, enable controlled human-robot interaction, but variability in users' physical attributes impacts consistency across individuals. Here, we present a method to standardize user effort in a physical tracking task, a common paradigm in rehabilitation and motor learning, where participants interact with a manipulator simulating a virtual environment. In addition to optimizing the initial configuration and scaling of the task trajectory, we analyze the effect of different scaling factors on the robot's virtual stiffness matrix, the impedance parameter that largely determines the effort required during the task. Simulations of nine virtual participants with varying heights and masses show that allometric scaling of robot's virtual stiffness by a factor of $(\mathrm{m} / \mathrm{h})^{2 / 3}$ best minimizes effort disparities. These findings advance the development of adaptive physical simulation platforms for rehabilitation, training, human motor control experiments, and human-robot collaboration.