Wiping 3D-objects using Deep Learning Model based on Image/Force/Joint Information

Abstract

We propose a deep learning model for a robot to wipe 3D-objects. Wiping of 3D-objects requires recognizing the shapes of objects and planning the motor angle adjustments for tracing the objects. Unlike previous research, our learning model does not require pre-designed computational models of target objects. The robot is able to wipe the objects to be placed by using image, force, and arm joint information. We evaluate the generalization ability of the model by confirming that the robot handles untrained cube and bowl shaped-objects. We also find that it is necessary to use both image and force information to recognize the shape of and wipe 3D objects consistently by comparing changes in the input sensor data to the model. To our knowledge, this is the first work enabling a robot to use learning sensorimotor information alone to trace various unknown 3D-shape.