  Robotic grasping is one of the most fundamental robotic manipulation tasks
and has been actively studied. However, how to quickly teach a robot to grasp a
novel target object in clutter remains challenging. This paper attempts to
tackle the challenge by leveraging object attributes that facilitate
recognition, grasping, and quick adaptation. In this work, we introduce an
end-to-end learning method of attribute-based robotic grasping with one-grasp
adaptation capability. Our approach fuses the embeddings of a workspace image
and a query text using a gated-attention mechanism and learns to predict
instance grasping affordances. Besides, we utilize object persistence before
and after grasping to learn a joint metric space of visual and textual
attributes. Our model is self-supervised in a simulation that only uses basic
objects of various colors and shapes but generalizes to novel objects and
real-world scenes. We further demonstrate that our model is capable of adapting
to novel objects with only one grasp data and improving instance grasping
performance significantly. Experimental results in both simulation and the real
world demonstrate that our approach achieves over 80\% instance grasping
success rate on unknown objects, which outperforms several baselines by large
margins.
