Learning impedance control of antagonistic systems based on stochastic optimization principles

Novel anthropomorphic robotic systems increasingly employ variable impedance actuation with a view to achieving robustness against uncertainty, superior agility and improved efficiency that are hallmarks of biological systems. Controlling and modulating impedance profiles such that they are optimally tuned to the controlled plant is crucial in realizing these benefits. In this work, we propose a methodology to generate optimal control commands for variable impedance actuators under a prescribed tradeoff of task accuracy and energy cost. We employ a supervised learning paradigm to acquire both the plant dynamics and its stochastic properties. This enables us to prescribe an optimal impedance and command profile (i) tuned to the hard-to-model plant noise characteristics and (ii) adaptable to systematic changes. To evaluate the scalability of our framework to real hardware, we designed and built a novel antagonistic series elastic actuator (SEA) characterized by a simple mechanical architecture and we ran several evaluations on a variety of reach and hold tasks. These results highlight, for the first time on real hardware, how impedance modulation profiles tuned to the plant dynamics emerge from the first principles of stochastic optimization, achieving clear performance gains over classical methods that ignore or are incapable of incorporating stochastic information.