# Lightning Grasp: High Performance Procedural Grasp Synthesis with Contact Fields

arXiv: https://arxiv.org/abs/2511.07418 · HTML: https://mlsanity.com/paper/2511.07418/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots](https://mlsanity.com/paper/1909.11639/) (2019)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [Twisting Lids Off with Two Hands](https://mlsanity.com/paper/2403.02338/) (2024)
- [DexterityGen: Foundation Controller for Unprecedented Dexterity](https://mlsanity.com/paper/2502.04307/) (2025)
- [Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation](https://mlsanity.com/paper/2303.03486/) (2023)
- [BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization](https://mlsanity.com/paper/2412.16490/) (2024)
- [Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy](https://mlsanity.com/paper/2504.18829/) (2025)
- [Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects](https://mlsanity.com/paper/2305.04654/) (2023)
- [SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty](https://mlsanity.com/paper/2404.13532/) (2024)
- [Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation](https://mlsanity.com/paper/2503.22370/) (2025)

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

- [Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation](https://mlsanity.com/paper/2602.15828/) (2026)
- [Mana: Dexterous Manipulation of Articulated Tools](https://mlsanity.com/paper/2606.13677/) (2026)

## Abstract (arXiv)

Despite years of research, real-time diverse grasp synthesis for dexterous hands remains an unsolved core challenge in robotics and computer graphics. We present Lightning Grasp, a novel high-performance procedural grasp synthesis algorithm that achieves orders-of-magnitude speedups over state-of-the-art approaches, while enabling unsupervised grasp generation for irregular, tool-like objects. The method avoids many limitations of prior approaches, such as the need for carefully tuned energy functions and sensitive initialization. This breakthrough is driven by a key insight: decoupling complex geometric computation from the search process via a simple, efficient data structure - the Contact Field. This abstraction collapses the problem complexity, enabling a procedural search at unprecedented speeds. We open-source our system to propel further innovation in robotic manipulation.