Deformable Energy Landscapes for Dexterous Grasp Generation

Dexterous grasp synthesis is typically formulated as conditional generation: a model samples hand configurations given an object, while separate stages rank, refine, or physically verify the results. In contrast, we learn a conditional energy field, a differentiable scalar over the hand's configuration space whose landscape is deformed by the target object, and synthesize grasps via gradient descent. Operating on a learned manifold of hand articulations, the field guides the hand entirely through its gradient. Since descent can start from any configuration, a single field can serve as a generator from uninformative initializations, as an operator to evaluate and refine the outputs or failures of other methods, and as a teacher for a motion prior. We instantiate this framework across graphics and robotics tasks, including static grasp synthesis and reach-and-grasp motion for the human hand, as well as grasp generation and cross-embodiment retargeting for robot hands. Our approach demonstrates competitive performance on all these distinct tasks.