In a series of studies over the last ten years, we have observed how the nervous system learns to coordinate muscles to produce both appropriate force and resistance to perturbation (mechanical impedance), at minimal metabolic cost. This is critical to understanding how we adapt motion in unstable situations in everyday life despite large motor noise, e.g., whenever we work with tools. To understand the mechanisms of this learning, we developed a computational model which predicts how sensory information is used to modify the motor commands to muscles during the whole learning process. We have recently implemented the human-like control algorithm on robot manipulators, which make them able to adapt force and impedance to interact efficiently with the environment, and to deal with inherent instability and forces. This novel human control algorithm opens new avenues for applications of robots interacting with humans, e.g. robotic rehabilitation, robotic wheelchair, or tremor attenuation. 

Etienne Burdet's main research interest is in human-machine interaction. He uses an approach integrating neuroscience and robotics to: i) investigate human motor control and ii) design efficient assistive devices and virtual reality based training for rehabilitation and surgery.