Manipulation with Shared Grasping
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Abstract (arXiv)
A shared grasp is a grasp formed by contacts between the manipulated object and both the robot hand and the environment. By trading off hand contacts for environmental contacts, a shared grasp requires fewer contacts with the hand, and enables manipulation even when a full grasp is not possible. Previous research has used shared grasps for non-prehensile manipulation such as pivoting and tumbling. This paper treats the problem more generally, with methods to select the best shared grasp and robot actions for a desired object motion. The central issue is to evaluate the feasible contact modes: for each contact, whether that contact will remain active, and whether slip will occur. Robustness is important. When a contact mode fails, e.g., when a contact is lost, or when unintentional slip occurs, the operation will fail, and in some cases damage may occur. In this work, we enumerate all feasible contact modes, calculate corresponding controls, and select the most robust candidate. We can also optimize the contact geometry for robustness. This paper employs quasi-static analysis of planar rigid bodies with Coulomb friction to derive the algorithms and controls. Finally, we demonstrate the robustness of shared grasping and the use of our methods in representative experiments and examples. The video can be found at https://youtu.be/tyNhJvRYZNk
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2025Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Correctionscited by 29
- 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexteritycited by 28
- 2023Robust Pivoting Manipulation using Contact Implicit Bilevel Optimizationcited by 3
- 2026Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learningcited by 2
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxationscited by 1
- 2026Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning10 Mar 2026
- 2025Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections20 Jun 2025
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations11 Mar 2025
- 2023Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization15 Mar 2023
- 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity2 Nov 2022