  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.
