Stochastic optimal control with variable impedance manipulators in presence of uncertainties and delayed feedback

Muscle co-contraction can be modeled as an active modulation of the passive musculo-skeletal compliance. Within this context, recent findings in human motor control have shown that active compliance modulation is fundamental when planning movements in presence of unpredictability and uncertainties. Along this line of research, this paper investigates the link between active impedance control and unpredictability, with special focus on robotic applications. Different types of actuators are considered and confronted to extreme situations such as moving in an unstable force field and controlling a system with significant delays in the feedback loop. We use tools from stochastic optimal control to illustrate the possibility of optimally planning the intrinsic system stiffness when performing movements in such situations. In the extreme case of total feedback absence, different actuators model are considered and their performance in dealing with unpredictability compared. Finally, an application of the proposed theories on planning reaching movements with the iCub humanoid platform is proposed.

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