  Semantic grasping is the problem of selecting stable grasps that are
functionally suitable for specific object manipulation tasks. In order for
robots to effectively perform object manipulation, a broad sense of contexts,
including object and task constraints, needs to be accounted for. We introduce
the Context-Aware Grasping Engine, which combines a novel semantic
representation of grasp contexts with a neural network structure based on the
Wide & Deep model, capable of capturing complex reasoning patterns. We
quantitatively validate our approach against three prior methods on a novel
dataset consisting of 14,000 semantic grasps for 44 objects, 7 tasks, and 6
different object states. Our approach outperformed all baselines by
statistically significant margins, producing new insights into the importance
of balancing memorization and generalization of contexts for semantic grasping.
We further demonstrate the effectiveness of our approach on robot experiments
in which the presented model successfully achieved 31 of 32 suitable grasps.
The code and data are available at:
https://github.com/wliu88/rail_semantic_grasping
