Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping

• URL: http://arxiv.org/abs/2410.23039v1
• Date: Wed, 30 Oct 2024 14:06:51 GMT
• Title: Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
• Authors: Qianxu Wang, Congyue Deng, Tyler Ga Wei Lum, Yuanpei Chen, Yaodong Yang, Jeannette Bohg, Yixin Zhu, Leonidas Guibas,
• Abstract summary: One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. We propose the textitneural attention field for representing semantic-aware dense feature fields in the 3D space.
• Score: 34.98831146003579
• License:
• Abstract: One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. While distilled feature fields from large vision models have enabled semantic correspondences across 3D scenes, their features are point-based and restricted to object surfaces, limiting their capability of modeling complex semantic feature distributions for hand-object interactions. In this work, we propose the \textit{neural attention field} for representing semantic-aware dense feature fields in the 3D space by modeling inter-point relevance instead of individual point features. Core to it is a transformer decoder that computes the cross-attention between any 3D query point with all the scene points, and provides the query point feature with an attention-based aggregation. We further propose a self-supervised framework for training the transformer decoder from only a few 3D pointclouds without hand demonstrations. Post-training, the attention field can be applied to novel scenes for semantics-aware dexterous grasping from one-shot demonstration. Experiments show that our method provides better optimization landscapes by encouraging the end-effector to focus on task-relevant scene regions, resulting in significant improvements in success rates on real robots compared with the feature-field-based methods.

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