# Manipulation with Shared Grasping

arXiv: https://arxiv.org/abs/2006.02996 · HTML: https://mlsanity.com/paper/2006.02996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025)
- [Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity](https://mlsanity.com/paper/2211.01500/) (2022)
- [Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization](https://mlsanity.com/paper/2303.08965/) (2023)
- [Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning](https://mlsanity.com/paper/2603.09882/) (2026)
- [Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations](https://mlsanity.com/paper/2503.07963/) (2025)

## 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