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Dexterous robotic hands have numerous sensors distributed over a flexible high-degree-of- freedom framework. Control of these hands often relies on a detailed task description that is either specified a priori or computed on-line from sensory feedback. Such controllers are complex and may use unnecessary precision. In contrast, one can incorporate plan cues that provide a contextual backdrop in order to simplify the control task. To demonstrate, a Utah/MIT dexterous hand mounted on a Puma 760 arm flips a plastic egg, using the finger tendon tensions as the sole control signal. The completion of each subtask, such as picking up the spatula, finding the pan, and sliding the spatula under the egg, is detected by sensing tension states. The strategy depends on the task context but does not require precise positioning knowledge. We term this qualitative manipulation to draw a parallel with qualitative vision strategies. The approach is to design closed-loop programs that detect significant events to control manipulation but ignore inessential details. The strategy is generalized by analyzing the robot state dynamics during teleoperated hand actions to reveal the essential features that control each action.
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