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We propose to learn to generate grasping motion for manipulation with adexterous hand using implicit functions. With continuous time inputs, the modelcan generate a continuous and smooth grasping plan. We name the proposed modelContinuous Grasping Function (CGF). CGF is learned via generative modeling witha Conditional Variational Autoencoder using 3D human demonstrations. We willfirst convert the large-scale human-object interaction trajectories to robotdemonstrations via motion retargeting, and then use these demonstrations totrain CGF. During inference, we perform sampling with CGF to generate differentgrasping plans in the simulator and select the successful ones to transfer tothe real robot. By training on diverse human data, our CGF allowsgeneralization to manipulate multiple objects. Compared to previous planningalgorithms, CGF is more efficient and achieves significant improvement onsuccess rate when transferred to grasping with the real Allegro Hand. Ourproject page is available at https://jianglongye.com/cgf .

Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations