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Camera viewpoint selection is an important aspect of visual grasp detection,especially in clutter where many occlusions are present. Where other approachesuse a static camera position or fixed data collection routines, our Multi-ViewPicking (MVP) controller uses an active perception approach to chooseinformative viewpoints based directly on a distribution of grasp pose estimatesin real time, reducing uncertainty in the grasp poses caused by clutter andocclusions. In trials of grasping 20 objects from clutter, our MVP controllerachieves 80% grasp success, outperforming a single-viewpoint grasp detector by12%. We also show that our approach is both more accurate and more efficientthan approaches which consider multiple fixed viewpoints.

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter