An Ergodic Approach to Robotic Surface Finishing With Learned Motion Preferences

Surface finishing is a time-consuming, dangerous task, difficult to automate despite its necessity in many manufacturing processes. Its automation, particularly through robotics, increases productivity and relieves workers from health-critical tasks. However, challenges remain, as automated offline planning tools can result in certain areas being either neglected or overly processed. Ergodic control offers the possibility to cover target probability distributions in an online manner, by taking into account the observed coverage history. However, existing ergodic control approaches provide little flexibility in designing and adapting coverage strategies. Moreover, they come with simplifying assumptions, such as point-based dynamics, which are no longer valid for tasks where the robot is in contact with strongly varying curvatures on non-trivial surface geometries. In this work, we introduce a closed-form ergodic control framework that includes the tool imprint in the system modeling while simultaneously permitting the intuitive transfer of finish strategies, namely preferred motion directions. We build on the Spectral Multiscale Coverage (SMC) approach, augmenting it with a tool imprint model, as well as both target distributions and state-dependent movement directions extracted from human demonstrations. Through evaluations in a surface finishing task using a torque-controlled, 7-DoF, robot arm we show that our approach optimally covers surfaces according to the tool contact area, with robust error convergence.