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.