mlsanityData to 7 Oct 2026

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

arXiv 2210.07420 ↗First posted 13 Oct 2022cs.RO cs.AI cs.LGcited by 5

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Abstract (arXiv)

We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase the number of potential grasps for a given group of objects, and thus increase picks per hour. We train a neural network using real examples to plan robust multi-object grasps. In physical experiments, we find a 13.7% increase in success rate, a 1.6x increase in picks per hour, and a 6.3x decrease in grasp planning time compared to prior work on multi-object grasping. Compared to single-object grasping, we find a 3.1x increase in picks per hour.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 54 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

17 of 54 references.

References · 17

  1. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
  2. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  3. 2017Domain Randomization and Generative Models for Robotic Graspingcited by 34
  4. 2016Funnel Libraries for Real-Time Robust Feedback Motion Planningcited by 28
  5. 2022ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulationcited by 24
  6. 2022Surprisingly Robust In-Hand Manipulation: An Empirical Studycited by 16
  7. 2017Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learningcited by 15
  8. 2020Occlusion-Aware Search for Object Retrieval in Cluttercited by 11
  9. 2021Collision-Aware Target-Driven Object Grasping in Constrained Environmentscited by 11
  10. 2021Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensorycited by 10
  11. 2021Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Graspcited by 5
  12. 2022Multi-Object Grasping in the Planecited by 5
5 more
  1. 2018Pushing Fast and Slow: Task-Adaptive Planning for Non-prehensile Manipulation Under Uncertaintycited by 3
  2. 2018Real-Time Online Re-Planning for Grasping Under Clutter and Uncertaintycited by 3
  3. 2021Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policycited by 3
  4. 2020Human-like Planning for Reaching in Cluttered Environmentscited by 2
  5. 2021Robust Physics-Based Manipulation by Interleaving Open and Closed-Loop Executioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 34
  2. 2024Stable Object Placement Planning From Contact Point Robustnesscited by 2
  3. 2025Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Deliverycited by 0
  4. 2025Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stackingcited by 0
  5. 2026RoboVista: Evaluating Vision Language Models for Diverse Robot Applicationscited by 0