Can robots find and grasp hidden objects?
Robots are not capable of handling tasks as simple as restocking grocery store shelves as they cannot perceive the environment as good as humans. What if we could give robots radio perception to search for items that are not in their sight? Such an ability will give them superhuman power to work in warehouses, stores, and our homes.
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 throughocclusions, 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.