S.B. Kang and K. Ikeuchi, ``Toward automatic robot instruction from perception -- Temporal segmentation of tasks from human hand motion,'' IEEE Trans. on Robotics and Automation, vol. 11, no. 5, Oct. 1995.


Our approach to program a robot is by direct human demonstration of the grasping task in front of the system. The system analyzes the stream of perceptual data measured during the human execution of the task and then produces commands to the robot system to replicate the observed task. In order to analyze the stream of perceptual data, it is easier to first segment this stream into meaningful and separate units for individual analysis. This paper describes work on the temporal segmentation of grasping task sequences based on human hand motion. The segmentation process results in the identification of motion breakpoints separating the different constituent phases of the grasping task. A grasping task is composed of three basic phases: pregrasp phase, static grasp phase, and manipulation phase.

We show that by analyzing the fingertip polygon area (which is an indication of the hand preshape) and the speed of hand movement (which is an indication of the hand transportation), we can divide a task into meaningful action segments such as approach object (which corresponds to the pregrasp phase), grasp object, manipulate object, place object, and depart (a special case of the pregrasp phase which signals the termination of the task). We introduce a measure called the {\em volume sweep rate}, which is the product of the fingertip polygon area and the hand speed. The profile of this measure is also used in the determination of the task breakpoints.

The temporal task segmentation process is important as it serves as a preprocessing step to the characterization of the task phases. Once the breakpoints have been identified, steps to recognize the grasp and extract the object motion can then be carried out.