Learning the dynamics of doors for robotic manipulation

Abstract

Opening doors is a fundamental skill for mobile robots operating in human environments. In this paper we present an approach to learn a dynamic model of a door from sensor observations and utilize it for effectively swinging the door open to a desired angle. The learned model enables the realization of dynamic door-opening strategies and reduces the complexity of the door opening task. For example, the robot does not need to maintain a grasp of the handle, which would form a closed kinematic chain. Accordingly, it reduces the degrees of freedom required of the manipulator and facilitates motion planning. Additionally, execution is faster, because the robot merely needs to push the door long enough to achieve the right combination of position and speed such that the door stops at the desired state. Our approach applies Gaussian process regression to learn the deceleration of the door with respect to position and velocity of the door. This model of the dynamics can be easily learned from observing a human teacher or by interactive experimentation.