Action gist based automatic segmentation for periodic in-hand manipulation movement learning

We consider in-hand manipulation tasks that consists of periodic movements. In order to improve the manipulation learning ability of a robot with a human-like hand, this paper introduces a segmentation method based on the techniques of action gist. Action gist is the key motion information in manipulation with the property of semantics. In the techniques of in-hand manipulation action gist, there is a Meta Motion Occurrence Histogram describing the motion information in the demonstration set. This paper proposes an algorithm related to the Meta Motion Occurrence Histogram to maximize the common motions in each segment, so as to figure out the best segmentation solution in the in-hand manipulation sequence. The experiments illustrate the performance of the proposed method, and discuss the possibility of segmentation fusing with the information from tactile sensor.