# Multi-Finger Grasping Like Humans

arXiv: https://arxiv.org/abs/2211.07304 · HTML: https://mlsanity.com/paper/2211.07304/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [Learning to Transfer Human Hand Skills for Robot Manipulations](https://mlsanity.com/paper/2501.04169/) (2025)
- [Learning a Universal Human Prior for Dexterous Manipulation from Human Preference](https://mlsanity.com/paper/2304.04602/) (2023)
- [HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching](https://mlsanity.com/paper/2604.10836/) (2026)
- [Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation](https://mlsanity.com/paper/2509.01242/) (2025)

## 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.