# Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting

arXiv: https://arxiv.org/abs/1902.10841 · HTML: https://mlsanity.com/paper/1902.10841/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

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

- [Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection](https://mlsanity.com/paper/2203.05187/) (2022)
- [Generative Grasp Detection and Estimation with Concept Learning-based Safety Criteria](https://mlsanity.com/paper/2506.17842/) (2025)

## Abstract (arXiv)

This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination. The MDISF algorithm searches for optimal contact regions and hand configurations by minimizing the collision and surface fitting error, and the GTO algorithm generates optimal finger trajectories to reach the highly ranked grasp configurations and avoid collision with the environment. The proposed grasp planning and imagination framework considers the collision avoidance and the kinematics of the hand-robot system, and is able to plan grasps and trajectories of different categories efficiently with gradient-based methods using the captured point cloud. The found grasps and trajectories are robust to sensing noises and underlying uncertainties. The effectiveness of the proposed framework is verified by both simulations and experiments.