The application discloses an optimization method for generating robust and physically feasible grasping poses for a
robot dexterous hand, belonging to the field of
robot control. The method comprises the following steps: step 1, predicting the 3D
affordance of an object using a conditional variational
autoencoder model, learning the geometric properties of the object in the
data set, taking the
point cloud of the object as the input, and outputting a 3D
affordance region map related to hand operation; step 2, the dexterous hand follows the 3D
affordance region map, based on the synergistic effect of the fingers and the palm, and generates a grasping
pose by optimizing the
total energy function containing a self-conflict optimization term between the fingers and a support optimization term, and the optimal grasping
pose of the
robot dexterous hand is obtained after optimization. The method uses a conditional variational
autoencoder model to predict a high-precision operation region, and introduces a self-conflict optimization term between the fingers and a support optimization term between the palm and the object to optimize the generation of the grasping
pose, realizes multi-finger coordination and grasping stability, and has robustness.