  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.
