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MIT robot uses radio waves to find and retrieve hidden objects

#artificialintelligence

MIT researchers have developed a robot that can detect and grab objects that are hidden behind walls or pieces of clutter. The system, called RF-Grasp, uses radio waves to locate items beyond the line-of-sight of a robot's cameras. It could help warehouse robots grab customer orders or tools that are occluded behind obstacles. If an object is concealed, they typically need to explore the environment and search for the item. Unlike visible light and infrared, RF (radio frequency) signals can traverse cardboard boxes, wooden walls, plastic covers, and colored glass to perceive objects fitted with RFID tags.


A robot that senses hidden objects

Robohub

In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib. In a new paper, Adib's team is pushing the technology a step further. "We're trying to give robots superhuman perception," he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects.


Robot That Senses Hidden Objects – "We're Trying to Give Robots Superhuman Perception"

#artificialintelligence

MIT researchers developed a picking robot that combines vision with radio frequency (RF) sensing to find and grasps objects, even if they're hidden from view. The technology could aid fulfilment in e-commerce warehouses. System uses penetrative radio frequency to pinpoint items, even when they're hidden from view. In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib.


Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators

arXiv.org Artificial Intelligence

Abstract-- 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.


A robot that senses hidden objects

#artificialintelligence

In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib. In a new paper, Adib's team is pushing the technology a step further. "We're trying to give robots superhuman perception," he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects.