mlsanityData to 14 Oct 2026

TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance

arXiv 2207.07861 ↗First posted 16 Jul 2022cs.RO cs.CVcited by 8

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Abstract (arXiv)

Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp pose estimation method that predicts grasp poses of a category of objects by labeling only one object instance. Specifically, we perform grasp pose transfer across a category of objects based on their shape correspondences and propose a grasp pose refinement module to further fine-tune grasp pose of grippers so as to ensure successful grasps. Experiments demonstrate the effectiveness of our method on achieving high-quality grasps with the transferred grasp poses. Our code is available at https://github.com/yanjh97/TransGrasp.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 35 references that are in the map. First posted on arXiv 16 Jul 2022.

Builds on

30 of 35 references.

References · 30

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  11. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
  12. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 209
18 more
  1. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  2. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 161
  3. 2018Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulationcited by 142
  4. 2020Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimationcited by 124
  5. 2021FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanismcited by 121
  6. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  7. 2020ACRONYM: A Large-Scale Grasp Dataset Based on Simulationcited by 117
  8. 2017Grasp Pose Detection in Point Cloudscited by 111
  9. 2021Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Cluttercited by 95
  10. 2020Deep Implicit Templates for 3D Shape Representationcited by 86
  11. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  12. 2016Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challengecited by 70
  13. 20196-DOF Grasping for Target-driven Object Manipulation in Cluttercited by 70
  14. 2019S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenescited by 51
  15. 2021CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulationcited by 49
  16. 2020Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Graspscited by 22
  17. 2020DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-based Robotic Graspingcited by 11
  18. 2020Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstructioncited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2022HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarioscited by 53
  2. 2025FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulationcited by 10
  3. 2024FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Objectcited by 6
  4. 2023Anthropomorphic Grasping with Neural Object Shape Completioncited by 4
  5. 2024Robot Instance Segmentation with Few Annotations for Graspingcited by 4
  6. 2024Adapting Segment Anything Model for Unseen Object Instance Segmentationcited by 1
  7. 2025ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulationcited by 0
  8. 2026Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrationscited by 0