  Developing personal robots that can perform a diverse range of manipulation
tasks in unstructured environments necessitates solving several challenges for
robotic grasping systems. We take a step towards this broader goal by
presenting the first RL-based system, to our knowledge, for a mobile
manipulator that can (a) achieve targeted grasping generalizing to unseen
target objects, (b) learn complex grasping strategies for cluttered scenes with
occluded objects, and (c) perform active vision through its movable wrist
camera to better locate objects. The system is informed of the desired target
object in the form of a single, arbitrary-pose RGB image of that object,
enabling the system to generalize to unseen objects without retraining. To
achieve such a system, we combine several advances in deep reinforcement
learning and present a large-scale distributed training system using
synchronous SGD that seamlessly scales to multi-node, multi-GPU infrastructure
to make rapid prototyping easier. We train and evaluate our system in a
simulated environment, identify key components for improving performance,
analyze its behaviors, and transfer to a real-world setup.
