Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous Robotics

• URL: http://arxiv.org/abs/2407.00614v1
• Date: Sun, 30 Jun 2024 07:42:57 GMT
• Title: Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous Robotics
• Authors: Fan Yang, Wenrui Chen, Kailun Yang, Haoran Lin, DongSheng Luo, Conghui Tang, Zhiyong Li, Yaonan Wang,
• Abstract summary: Affordance features of objects serve as a bridge in the functional interaction between agents and objects. We propose a granularity-aware affordance feature extraction method for locating functional affordance areas. We also use highly activated coarse-grained affordance features in hand-object interaction regions to predict grasp gestures. This forms a complete dexterous robotic functional grasping framework GAAF-Dex.
• Score: 27.124273762587848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features of objects serve as a bridge in the functional interaction between agents and objects. However, leveraging these affordance cues to help robots achieve functional tool grasping remains unresolved. To address this, we propose a granularity-aware affordance feature extraction method for locating functional affordance areas and predicting dexterous coarse gestures. We study the intrinsic mechanisms of human tool use. On one hand, we use fine-grained affordance features of object-functional finger contact areas to locate functional affordance regions. On the other hand, we use highly activated coarse-grained affordance features in hand-object interaction regions to predict grasp gestures. Additionally, we introduce a model-based post-processing module that includes functional finger coordinate localization, finger-to-end coordinate transformation, and force feedback-based coarse-to-fine grasping. This forms a complete dexterous robotic functional grasping framework GAAF-Dex, which learns Granularity-Aware Affordances from human-object interaction for tool-based Functional grasping in Dexterous Robotics. Unlike fully-supervised methods that require extensive data annotation, we employ a weakly supervised approach to extract relevant cues from exocentric (Exo) images of hand-object interactions to supervise feature extraction in egocentric (Ego) images. We have constructed a small-scale dataset, FAH, which includes near 6K images of functional hand-object interaction Exo- and Ego images of 18 commonly used tools performing 6 tasks. Extensive experiments on the dataset demonstrate our method outperforms state-of-the-art methods. The code will be made publicly available at https://github.com/yangfan293/GAAF-DEX.

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