Abstract: Physical human-robot interactions (pHRI) are promising for a large panel of public health applications, ranging from MSD prevention to neurorehabilitation. However, physical interaction control methods often neglect fundamental aspects of human motor control, which directly impact the outcomes of the interaction. Here, I will first present two recent studies showing how time plays a major role in the planning of human movements, both alone and when interacting with a human partner, making it a critical aspect to account for in pHRI. Then, I will present recent results showing how such knowledge on human motor control strategies can be accounted for using a multi-agent representation of pHRI.

Bio: After graduating from ENS Paris-Saclay in mechanical engineering in 2019, I did my PhD at Université Paris-Saclay under the supervision of B. Berret and O. Bruneau on human-exoskeleton interactions, at the interface between robotics and human motor control. I was then invited to join the group of E. Burdet at Imperial College London, where I was a postdoctoral fellow for two years. There, I worked on human sensorimotor augmentation with supernumerary robotic limbs, human motor control, and human-human physical interactions. I was recently recruited as a postdoctoral fellow on the ASSISTMOV project of the PEPR O2R, in Université Paris-Saclay, to work on the integration of differential games-based control, EMG-based human joints torque estimation, and AI-based movement prediction.