To alleviate the problem of overwhelming complexity in grasp synthesis and path planning associated with robot task planning, we adopt the approach of teaching the robot by demonstrating in front of it. A system with this programming technique is able to temporally segment a task into separate and meaningful parts for further individual analysis and recognize the human grasp employed in the task. With such derived information, this system would then map the human grasp to that of the given manipulator, plan its trajectory, and proceed to execute the task.

This paper describes how grasp mapping can be accomplished in our system. The mapping process essentially comprises three steps. The first step is local functional mapping, in which grasps of functionally equivalent fingers are established. This is followed by gross physical mapping which produces a kinematically feasible manipulator grasp. Finally, by carrying out local grasp adjustment using some task-related criterion, we arrive at a locally optimal manipulator grasp. We describe these steps in detail in this paper and show results of example grasp mappings.