GraspPC: Generating Diverse Hand Grasp Point Clouds on Objects

We present GraspPC, an approach to perform learning-based synthesis of multiple human hand grasps as point clouds from point clouds of objects. GraspPC benefits human-robot handover approaches by providing hypotheses of human grasp on objects to inform robotic manipulation algorithms on how to bias robotic grasp for safe handover. Existing learning-based approaches to conduct hand grasp prediction require datasets to contain annotated articulated hand models, making them difficult to train on datasets that lack hand model annotations. GraspPC treats the problem of hand point cloud generation from object point clouds as a set-to-set translation problem. We contribute a Transformer architecture to synthesize point clouds via GraspPC. To generate diverse hand grasps, we generate multiple object-dependent queries and train the network using a winner-takes-gradient strategy. We show results of diverse grasps by training and testing on a variety of real-world datasets. We demonstrate how human grasps generated by GraspPC can be used to filter robotic grasp candidates to inform human-robot handover.

In IEEE International Conference on Robot and Human Interactive Communication