Recalling Candidates of Grasping Method from an Object Image using Neural Network

Abstract

Robots are required to support people’s work. In order to alleviate the burden on people, it is desirable that robot can automatically generate and execute complicated motions according to simple directions from people. However, there are multiple grasping methods for one object. In order to select a motion suitable for the direction, it is important to estimate candidates of grasping method for the object. In this research, we purpose to recall candidates of grasping position and hand shape from an object image. In learning, a network that outputs a plurality of grasping method candidates for one object image to each channel of a multi-channel image is used. At this time, a plurality of grasping methods are not learned at same time, learned one by one. The similar grasping method for the similar object shape is automatically clustered to each output channel in the learning process, and a grasping method having a characteristic difference is presented as a candidate. We show the usefulness of this method using experimental examples.