# Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

arXiv: https://arxiv.org/abs/1904.07332 · HTML: https://mlsanity.com/paper/1904.07332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection](https://mlsanity.com/paper/2312.10571/) (2023)

## Abstract (arXiv)

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.