SpringGrasp: Synthesizing Compliant Dexterous Grasps under Shape Uncertainty

Sirui Chen1
Jeannette Bohg1
C. Karen Liu1

Stanford University

Teaser figure.
Planning grasp for different object with single depth image input.


Generating stable and robust grasps on generic 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.


Grasping different objects

Grasping object at different pose

Compliant grasp resist perturbation

Compared with force closure baseline


            title={SpringGrasp: An optimization pipeline for robust and compliant dexterous pre-grasp synthesis}, 
            author={Sirui Chen and Jeannette Bohg and C. Karen Liu},


This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project. It was adapted to be mobile responsive by Jason Zhang for PHOSA. The code can be found here.