  We present the design, implementation, and evaluation of RF-Grasp, a robotic
system that can grasp fully-occluded objects in unknown and unstructured
environments. Unlike prior systems that are constrained by the line-of-sight
perception of vision and infrared sensors, RF-Grasp employs RF (Radio
Frequency) perception to identify and locate target objects through occlusions,
and perform efficient exploration and complex manipulation tasks in
non-line-of-sight settings.
  RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached
to objects of interest. It introduces two main innovations: (1) an RF-visual
servoing controller that uses the RFID's location to selectively explore the
environment and plan an efficient trajectory toward an occluded target, and (2)
an RF-visual deep reinforcement learning network that can learn and execute
efficient, complex policies for decluttering and grasping.
  We implemented and evaluated an end-to-end physical prototype of RF-Grasp and
a state-of-the-art baseline. We demonstrate it improves success rate and
efficiency by up to 40-50% in cluttered settings. We also demonstrate RF-Grasp
in novel tasks such mechanical search of fully-occluded objects behind
obstacles, opening up new possibilities for robotic manipulation.
