# SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty

arXiv: https://arxiv.org/abs/2404.13532 · HTML: https://mlsanity.com/paper/2404.13532/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2024.

## Built on by (most cited first, 8 of 8)

- [CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation](https://mlsanity.com/paper/2512.14689/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Lightning Grasp: High Performance Procedural Grasp Synthesis with Contact Fields](https://mlsanity.com/paper/2511.07418/) (2025)
- [UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis](https://mlsanity.com/paper/2607.28198/) (2026)
- [EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning](https://mlsanity.com/paper/2606.18092/) (2026)
- [AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection](https://mlsanity.com/paper/2606.23689/) (2026)
- [Mana: Dexterous Manipulation of Articulated Tools](https://mlsanity.com/paper/2606.13677/) (2026)
- [GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive](https://mlsanity.com/paper/2610.03861/) (2026)

## Abstract (arXiv)

Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncertain observations of the object surface for synthesizing compliant dexterous grasps. A compliant dexterous grasp could minimize the effect of unexpected contact with the object, leading to more stable grasp with shape-uncertain objects. We introduce an analytical and differentiable metric, SpringGrasp metric, that evaluates the dynamic behavior of the entire compliant grasping process. Planning with SpringGrasp planner, our method achieves a grasp success rate of 89% from two viewpoints and 84% from a single viewpoints in experiment with a real robot on 14 common objects. Compared with a force-closure based planner, our method achieves at least 18% higher grasp success rate.