The application discloses a dexterous hand grasping method based on a fine-grained contact representation of a
diffusion model, and particularly relates to the technical field of robots, and comprises the following steps: constructing a three-stage framework of "contact map generation-contact matrix prediction-grasping
pose generation", designing a
diffusion model-based interdigital contact map generation model, predicting the contact area and direction of each fingertip from an
object point cloud, and generating a fine-grained contact matrix map; using a direction consistency optimization
algorithm, fusing physical constraints and a contact map, and ensuring that the
force direction of the fingers matches the surface normal of the object; solving the interdigital contact accuracy problem through multi-
task learning and other methods, performing physical
robot experiments on platforms such as Shadow Hand to verify the actual grasping effect, generating an independent contact probability matrix map for each finger, and the generated contact probability matrix map fuses the direction of the grasped object; and adopting a hierarchical optimization strategy,
reinforcement learning assisted optimization and lightweight model construction, and improving the
dynamic contact optimization efficiency.