Beyond Fingertips: From Palm Priors to Tactile Refinement for Generalizable Dexterous Grasping

Learning-based dexterous grasp synthesis has shown strong performance on training distributions, but often generalizes poorly to novel objects even when the generated hand poses satisfy kinematic and force-closure constraints. We argue that pose validity alone is insufficient for reliable grasp execution because it does not explicitly account for palm placement or the contact state established after execution. To address this limitation, we formulate dexterous grasping using three complementary components: pose generation, palm-anchor prediction, and tactile refinement. Our method first predicts a dense palm-anchor quality field over the object surface and uses the selected anchor to condition a hierarchical diffusion model for full-hand grasp generation. The resulting grasp is refined by an actor–critic policy using whole-hand tactile observations organized by the hand's kinematic graph. Our tactile infrastructure and refinement policy share this morphology-parameterized graph interface, allowing the same policy architecture to be instantiated and trained separately for hands with different link counts, degrees of freedom, and sensor layouts without redesigning the tactile representation. Experiments on two hand morphologies, using independently trained policies and hand-specific data budgets, validate this architectural portability. On in-distribution and unseen object categories, our method achieves 79.30% grasp success on unseen objects, outperforming the strongest baseline by 8.0 percentage points. Without real-world grasp training, the method further attains zero-shot aggregate success rates of 43.3% on the Wuji Hand and 31.7% on LinkerHand O6, evaluated on separate household-object sets.