Counting Objects in a Robotic Hand
arxiv(2024)
摘要
A robot performing multi-object grasping needs to sense the number of objects
in the hand after grasping. The count plays an important role in determining
the robot's next move and the outcome and efficiency of the whole pick-place
process. This paper presents a data-driven contrastive learning-based counting
classifier with a modified loss function as a simple and effective approach for
object counting despite significant occlusion challenges caused by robotic
fingers and objects. The model was validated against other models with three
different common shapes (spheres, cylinders, and cubes) in simulation and in a
real setup. The proposed contrastive learning-based counting approach achieved
above 96% accuracy for all three objects in the real setup.
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