Multi-Finger Grasping Like Humans
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Abstract (arXiv)
Robots with multi-fingered grippers could perform advanced manipulation tasks for us if we were able to properly specify to them what to do. In this study, we take a step in that direction by making a robot grasp an object like a grasping demonstration performed by a human. We propose a novel optimization-based approach for transferring human grasp demonstrations to any multi-fingered grippers, which produces robotic grasps that mimic the human hand orientation and the contact area with the object, while alleviating interpenetration. Extensive experiments with the Allegro and BarrettHand grippers show that our method leads to grasps more similar to the human demonstration than existing approaches, without requiring any gripper-specific tuning. We confirm these findings through a user study and validate the applicability of our approach on a real robot.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Nov 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025Learning to Transfer Human Hand Skills for Robot Manipulationscited by 11
- 2023Learning a Universal Human Prior for Dexterous Manipulation from Human Preferencecited by 10
- 2026HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matchingcited by 1
- 2025Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimationcited by 0
- 2026HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching12 Apr 2026
- 2025Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation1 Sep 2025
- 2025Learning to Transfer Human Hand Skills for Robot Manipulations7 Jan 2025
- 2023Learning a Universal Human Prior for Dexterous Manipulation from Human Preference10 Apr 2023