Brainwave Based on the team’s background and research in deep imitation learning, deep reinforcement learning and meta-learning, Covariant’s vision is the Covariant Brain – a universal AI system for robots designed to be applied to any use case or customer environment. Te robots learn general abilities, such as robust 3D vision, physical properties of objects, few-shot learning and real-time motion planning. Tis enables them to learn to manipulate objects without being told what to do. As with all new technology, it is reaching the commercial stage for continuous production that often proves to be the challenge. Tis is because there are an almost unlimited number of scenarios that a robot could encounter, so it’s impossible to program each one. In order to operate and add value to customer environments, a robot must be able to see, understand, make decisions, learn from mistakes and adapt to change, on its own. Te Covariant Brain is designed to
enable the robots to do all of these things continuously in real-world environments.
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‘Developing safe and reliable robotic technologies to address challenges in the real world is extremely hard’
Abbeel said: ‘Even though we are just
getting started, the systems we have deployed in Europe and North America are already learning from one another and improving every day.’ Raquel Urtasun, chief scientist at Uber
Advanced Technologies Group, and a Covariant investor, added: ‘Developing safe and reliable robotic technologies to address challenges in the real world is extremely hard. Whether you’re talking about self-driving cars or warehouse operations, robots encounter an endless number of unexpected scenarios. Covariant has demonstrated exceptional progress on enabling robots to fill orders in warehouses, which could unlock many other robotic manipulation tasks in other industries.’
Take your pick One of the biggest challenges when it comes to automation in warehouses, said Mark Williamson, group marketing director at Stemmer Imaging, is bin picking. Programming a robot to pick up random objects from boxes relieves people of this task. Te issue in logistics, says Williamson, is that robots are asked to handle a far wider range of items than is the case in a factory, which makes the imaging and gripping aspects a lot more complicated. While a number of companies produce
general bin picking tools, Williamson feels they all typically target one kind of imaging technique. ‘Tey might use a low-cost stereo camera; they might use a laser profiling system, or a fringe projection kind of system,’ he explained. Each technique has advantages and disadvantages, but while one 3D method might work well in a certain scenario and with certain equipment, it might not work for a different application. Stemmer Imaging’s solution to this dilemma came following the acquisition of Infaimon, a provider of software and hardware for machine vision and robotics.
g FEBRUARY/MARCH 2020 IMAGING AND MACHINE VISION EUROPE 29
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