Designing Variable Impedance Policies: A Novel Framework
Recent advances in VIA design has equipped prototype robots with the ability to change their stiffness and damping during task execution. How can we maximally exploit this capability? In this talk, I will look at impedance modulation in three different classes of movements: point-to-point tasks like reaching, explosive movement tasks like throwing and rhythmic movement tasks such as walking. I will describe an optimal control based formulation of optimizing both the temporal profile of movement and impedance modulation in a way that is tuned to the dynamics of the plant. Several hardware tests will serve to highlight the benefits. Further, I will illustrates the pitfalls of naively mimicking impedance profiles across heterogeneous systems (e.g., human limb to VS joints or MACCEPA actuators) and describe a framework that is capable of abstracting out the specific plant dynamics while ensuring task optimality. This talk will draw upon concepts of optimal feedback control, apprenticeship learning and model free reinforcement learning besides fundamentals of dynamics representation and learning.