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Multi-Object Grasping in the Plane

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arxiv 2206.00229 v2 pith:ADY75IEF submitted 2022-06-01 cs.RO cs.AI

classification cs.ROcs.AI
keywords multi-objectgraspingobjectsgraspbaselinefasterfindfrictionless
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We consider a novel problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface visible from an overhead camera. The objective is to efficiently grasp and transport all objects into a bin using multi-object push-grasps, where multiple objects are pushed together to facilitate multi-object grasping. We provide necessary conditions for frictionless multi-object push-grasps and apply these to filter inadmissible grasps in a novel multi-object grasp planner. We find that our planner is 19 times faster than a Mujoco simulator baseline. We also propose a picking algorithm that uses both single- and multi-object grasps to pick objects. In physical grasping experiments comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13.6\% higher grasp success and is 59.9\% faster, from 212 PPH to 340 PPH. See \url{https://sites.google.com/view/multi-object-grasping} for videos and code.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hearing the Slide: Acoustic-Guided Constraint Learning for Fast Non-Prehensile Transport

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A contact microphone on a robot tray learns a velocity-dependent friction constraint that reduces object displacement during fast non-prehensile transport by an average of 86% in physical experiments.

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