mlsanityData to 14 Oct 2026

Counting Objects in a Robotic Hand

arXiv 2404.06631 ↗First posted 9 Apr 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 9 Apr 2024.

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21 of 36 references.

References · 21

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9 more
  1. 2022Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Countingcited by 80
  2. 2015Active Object Localization with Deep Reinforcement Learningcited by 42
  3. 2022Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervisioncited by 41
  4. 2021Research Challenges and Progress in Robotic Grasping and Manipulation Competitionscited by 9
  5. 2021Detecting and Counting Oysterscited by 7
  6. 2021Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Graspcited by 5
  7. 2022Multi-Object Grasping -- Types and Taxonomycited by 5
  8. 2015Occlusion-Aware Object Localization, Segmentation and Pose Estimationcited by 4
  9. 2021Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policycited by 3

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