Deep Compliant Control
Seunghwan Lee, Phil Sik Chang, Jehee Lee
Abstract
In many physical interactions such as opening doors and playing sports, humans act compliantly to move in various ways to avoid large impacts or to manipulate objects. This paper aims to build a framework for simulation and control of humanoids that creates physically compliant interactions with surroundings. We can generate a broad spectrum of movements ranging from passive reactions to external physical perturbations, to active manipulations with clear intentions. Technical challenges include defining compliance, reproducing physically reliable movements, and robustly controlling under-actuated dynamical systems. The key technical contribution is a two-level control architecture based on deep reinforcement learning that imitates human movements while adjusting their bodies to external perturbations. The controller minimizes the interaction forces and the control torques for imitation, and we demonstrate the effectiveness of the controller with various motor skills including opening doors, balancing a ball, and running hand in hand.