In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a spoon level to avoid spills or properly orienting a finger to push a button. We introduce an asymptotically optimal sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. We demonstrate the method's effectiveness using a small humanoid robot (an Aldebaran Nao robot) performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints.
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robotics.cs.unc...
Негізгі бет Robot Motion Planning for Learned Tasks
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