Hand-object information embedded dexterous grasping generation

Given the visual data of an object, directly predicting the high-degree-of-freedom grasping pose of a dexterous hand is a challenging task. In this paper, we propose a new grasp synthesis framework based on hand-object interaction for representing the grasping pose of a high degree of freedom hand. First, the attention operation embedded in the finger encoder guides the contact of each finger with the object to obtain the initial grasp pose; then, based on the hand-object interaction constraints, the pose is further optimized in the finger refinement module. The experimental results show that our model enables the robot to grasp objects stably.