EETimes - Neuromorphic Sensor Fusion Lets Robots Grip, Identify Objects

Researchers at the National University of Singapore recently demonstrated the advantages of using neuromorphic sensor fusion to help robots grip and identify objects. It's just one of a number of interesting projects they've been working on including developing a new protocol for transmitting tactile data, building a neuromorphic tactile fingertip, and developing new visual-tactile datasets for the development of better learning systems. Because the technology uses address-events and spiking neural networks it is extremely power efficient: 50 times more using one of the Intel Loihi neuromorphic chips than a GPU. However, what's particularly elegant about this work is that it points the way towards neuromorphic technology as a means of efficiently integrating -- and extracting meaning from -- many different sensors for complex tasks in power-constrained systems. The new tactile sensor they used, NeuTouch, consists of an array of 39 taxels (tactile pixels) and the movement is transduced using a graphene-based piezo-resistive layer; you can think as this as the front of the robot's fingertip.