視覚情報に基づく食器類の把持の冗長性を考慮した自己教師あり把持学習

抄録

In the research of object grasping, systems that output consistent results from recognition to grasp motion have been actively studied. Usually, a single grasp point is determined even though an object such as tableware has redundancy to be grasped. In addition, it is difficult to reflect the input constraints due to the robot’s hardware or the surrounding environment. In this study, we propose a neural network that modifies the grasp pose around the initial pose from visual information and the actual trial. Our system can autonomously collect supervised data so that the robot can learn by itself. Since the search points are narrowed down to the edge points of the object, the real robot can efficiently acquire the grasp ability in fewer trials. As a result, it can grasp unknown objects, and flexibly change its grasp position because the input can be easily constrained.