Title: End-to-End Learning of Semantic Grasping,
Abstract: We consider the task of semantic robotic grasping, in which a robot picks up
an object of a user-specified class using only monocular images. Inspired by
the two-stream hypothesis of visual reasoning, we present a semantic grasping
framework that learns object detection, classification, and grasp planning in
an end-to-end fashion. A "ventral stream" recognizes object class while a
"dorsal stream" simultaneously interprets the geometric relationships necessary
to execute successful grasps. We leverage the autonomous data collection
capabilities of robots to obtain a large self-supervised dataset for training
the dorsal stream, and use semi-supervised label propagation to train the
ventral stream with only a modest amount of human supervision. We
experimentally show that our approach improves upon grasping systems whose
components are not learned end-to-end, including a baseline method that uses
bounding box detection. Furthermore, we show that jointly training our model
with auxiliary data consisting of non-semantic grasping data, as well as
semantically labeled images without grasp actions, has the potential to
substantially improve semantic grasping performance.