Visual perception of limb stiffness

Abstract

For robotic systems to interact with or learn from the actions of surrounding humans, it is important that they can accurately interpret the intention driving human motor actions. Making such interpretations, however, requires the ability to perceive the relevant feature(s) from the observed human behavior. With visual sensing alone, robots are typically limited to perceiving only the human's overt motion in the form of joint angles and positions. Ideally, robots designed to interface with humans would also be able to infer information as to how the human is controlling itself from that overt motion. In this study, we investigated if and how humans might be able to visually sense changes in limb mechanical impedance of others. Results indicated that humans can visually perceive changes in joint stiffness from the motion of a two-link planar arm, suggesting that humans can extract information regarding how humans control limb impedance from kinematic information. These findings have important implications for applications where robots must interpret the motor actions of humans, such as during robot imitation learning and human-robot physical interaction.