Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization

To fully utilize the versatility of a multi-finger dexterous robotic hand for object grasping, one must satisfy complex physical constraints introduced by hand-object interaction and object geometry during grasp planning. We propose an integrative approach of combining a generative model and a bilevel optimization to compute diverse grasps for novel unseen objects. First, a grasp prediction is obtained from a conditional variational autoencoder trained on merely six YCB objects. The prediction is then projected onto the manifold of kinematically and dynamically feasible grasps by jointly solving collision-aware inverse kinematics, force closure, and friction constraints as one nonconvex bilevel optimization. We demonstrate the effectiveness of our method on hardware by successfully grasping a wide range of unseen household objects, including adversarial shapes challenging to other types of robotic grippers. A video summary of our results is available at https://youtu.be/9DTrImbN99I.