GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping

As we aspire to solve more dexterous tasks in robotics, multi-finger grasping becomes of increasing importance. However, the varying degrees of freedom (DoF) of end-effectors and high multimodality of grasping modes depending on both end-effectors and objects, still pose open challenges. Previous works in grasping focus on parallel grippers [1, 2, 3], a single multi-finger gripper [4, 5, 6, 7], or a shared policy for multiple dexterous grippers [8, 9, 10, 11]. However, even methods that explore cross-embodiment mostly focus on generalization to unseen objects, and still show limited zero-shot generalization to unseen grippers. In this work, we propose GeoMatch++, a multi-embodiment grasping method which improves out-of-domain generalization on unseen grippers by leveraging robot morphology. Intuitively, robot morphology is essential to grasping - various end-effectors may have a different number of fingers, but fingertips and palm tend to be the most frequent contact regions. Thus, we hypothesize that learning good morphology embeddings can lead to a transferable grasping policy between different robots. Our main contribution is learning geometry correlation features between objects and end-effector morphology, which improve out-of-domain grasp success by 9.64% compared to previous methods, and our method showcases a minimal decrease in performance compared to in-domain evaluation.