Versatile kinematics-based constraint identification applied to robot task reproduction

Abstract

Identifying kinematic constraints between a robot and its environment can improve autonomous task execution, for example, in Learning from Demonstration. Constraint identification methods in the literature often require specific prior constraint models, geometry or noise estimates, or force measurements. Because such specific prior information or measurements are not always available, we propose a versatile kinematics-only method. We identify constraints using constraint reference frames, which are attached to a robot or ground body and may have zero-velocity constraints along their axes. Given measured kinematics, constraint frames are identified by minimizing a norm on the Cartesian components of the velocities expressed in that frame. Thereby, a minimal representation of the velocities is found, which represent the zero-velocity constraints we aim to find. In simulation experiments, we identified the geometry (position and orientation) of twelve different constraints including articulated contacts, polyhedral contacts, and contour following contacts. Accuracy was found to decrease linearly with sensor noise. In robot experiments, we identified constraint frames in various tasks and used them for task reproduction. Reproduction performance was similar when using our constraint identification method compared to methods from the literature. Our method can be applied to a large variety of robots in environments without prior constraint information, such as in everyday robot settings.