Last week, Sony and Carnegie Mellon University announced a collaboration "on artificial intelligence (AI) and robotics research." Usually, these announcements pretty much just end there, with the implication being that giant corporation X will support academic research institution Y by funding ongoing research or a string of new initiatives. This Sony/CMU announcement is a bit more exciting because of how specific it is: The project will be about food. Researchers will focus on defining the domain of food ordering, preparation, and delivery. Initially, they will build upon existing manipulation robots and mobile robots, and will plan on developing new domain-specific robots for predefined food preparation items and for mobility in a limited confined space.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With all the hype about SpotMini recently, it's a good time to take a look back at another quadruped that Boston Dynamics helped develop. This system is the first of its kind that can automatically keep a cluttered room neat and tidy at a practical level, something that has been difficult to achieve using conventional robot system.
Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.
Sheer cliff faces present a traversal challenge for most wheeled robots on the market, but researchers at the University of Tokyo say they've developed a two-robot framework that works pretty reliably in their testing. In a newly published paper on the preprint server Arxiv.org "[We] propose a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device," the authors of the paper explain. "[It enhances] the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether." The UGV is permanently attached via mechanized winch and cable to the UAV, a custom-made quadcopter with an Nvidia Jetson TX2 chipset, a flight controller, and a raft of sensors including a modular fisheye camera, time-of-flight sensor, inertial measurement unit (IMU), and laser sensor.