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GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping

arXiv 2210.03628 ↗First posted 7 Oct 2022cs.RO cs.AIcited by 0

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Abstract (arXiv)

As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able to grasp and manipulate novel objects in various situations. This paper presents GraspCaps, a novel architecture based on Capsule Networks for generating per-point 6D grasp configurations for familiar objects. GraspCaps extracts a rich feature vector of the objects present in the point cloud input, which is then used to generate per-point grasp vectors. This approach allows the network to learn specific grasping strategies for each object category. In addition to GraspCaps, the paper also presents a method for generating a large object-grasping dataset using simulated annealing. The obtained dataset is then used to train the GraspCaps network. Through extensive experiments, we evaluate the performance of the proposed approach, particularly in terms of the success rate of grasping familiar objects in challenging real and simulated scenarios. The experimental results showed that the overall object-grasping performance of the proposed approach is significantly better than the selected baseline. This superior performance highlights the effectiveness of the GraspCaps in achieving successful object grasping across various scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 7 Oct 2022.

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23 of 33 references.

References · 23

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2017Dynamic Routing Between Capsulescited by 689
  5. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  6. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 237
  7. 2015Order Matters: Sequence to sequence for setscited by 236
  8. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
  9. 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 117
  10. 2017Grasp Pose Detection in Point Cloudscited by 107
  11. 2021Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Cluttercited by 94
  12. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
11 more
  1. 2022Deep Learning Approaches to Grasp Synthesis: A Reviewcited by 69
  2. 2019S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenescited by 51
  3. 2021GPR: Grasp Pose Refinement Network for Cluttered Scenescited by 13
  4. 2022When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detectioncited by 12
  5. 2021MVGrasp: Real-Time Multi-View 3D Object Grasping in Highly Cluttered Environmentscited by 10
  6. 2021Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domainscited by 10
  7. 2019Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulationcited by 6
  8. 2022Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognitioncited by 6
  9. 20183DCapsule: Extending the Capsule Architecture to Classify 3D Point Cloudscited by 5
  10. 2017The Effect of Color Space Selection on Detectability and Discriminability of Colored Objectscited by 3
  11. 2021Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networkscited by 2

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