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VISUAL PERCEPTION OF HUMAN AND ROBOT MOTOR CONTROL

Humans have an astonishing ability to extract hidden information from the movements of others. For example, even with limited kinematic information, humans can distinguish between biological and nonbiological motion, identify the age and gender of a human demonstrator, and recognize what action a human demonstrator is performing. In this research, we are testing whether humans can also estimate hidden mechanical properties of another’s limbs simply by observing their motions. Strictly speaking, identifying an object’s mechanical properties, such as stiffness, requires contact. With only motion information, unambiguous measurements of stiffness are fundamentally impossible, since the same limb motion can be generated with an infinite number of stiffness values. Our results indicate that humans can readily estimate the stiffness of a simulated limb from its motion, and our current research aims to identify how.

 

HUMAN ADAPTATION TO ROBOT SYSTEMS

Robots are unlikely any complex machines that humans have previously experienced. Thus, we treat human robot collaboration as a learning problem on the part of the human. In this line of research, we aim to identify how the human nervous system innately responds to wearable robots and develop methods to effectively guide the human nervous system to learn behaviors to achieve a desired behavior of the coupled human-robot systems. Conducting this research requires the building and use of custom wearable robotic systems.