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. Qualitative results (videos) available at rfgrasp.media.mit.edu

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Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fullyoccluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (e.g., RFID), making their approach beneficial only to tagged items in the environment.We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RFtagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions.We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 180 real-world experimental trials to evaluate FuseBot and compare its performance to a state-ofthe-art vision-based system named X-Ray [10]. Our experimental results demonstrate that FuseBot outperforms X-Ray's efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray's success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonstrating for the first time that the benefits of RF perception extend beyond tagged objects in the mechanical search problem.

Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fully-occluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (eg, RFID), making their approach beneficial only to tagged items in the environment. We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RF-tagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions. We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 180 real-world experimental trials to evaluate FuseBot and compare its performance to a state-of-the-art vision-based system named X-Ray. Our experimental results demonstrate that FuseBot outperforms X-Ray’s efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray’s success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonstrating for the first time that the benefits of RF perception extend beyond tagged objects in the mechanical search problem.

We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fullyoccluded 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 RFvisual 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.