Getting AI to work in a fleshy, messy world is harder than you think
At the warehouses of British online grocery company Ocado Technology, robots, guided by AI, whizz around on rails at speeds of up to four metres per second, picking a 50-item order in minutes. The journeys then taken by Ocado’s delivery trucks are optimised by a neural network that makes more than 14 million last-mile routing calculations per second, and adjusts delivery routes each time a customer places a new order or adds extra items to their shopping lists.
But Ocado’s most ambitious automation efforts involve packing robots. At the time of writing the company has five robotic picking arms powered by computer vision, and other machine-learning systems that can identify the products that need to be packed and use suction power to grab them. Further advances, undertaken in conjunction with two European academic-led projects, are in the pipeline.
Picking and packing aren’t easy if you’re a robot. “From a human’s perspective, it is a fairly simple task to pick and pack, and it doesn’t require an awful lot of training,” says Alex Harvey, chief of advanced technology at Ocado Technology. “For a computer and for a robot, the dexterous manipulation involved is far beyond the state of the art today to be able to pick and pack the full range of items that we do.”
Ocado’s Robotic Suction Pick (RSP) machine is a vacuum cup, powered by an air compressor, that sits at the end of an articulated arm. It uses computer vision and built-in sensors to select items gathered by a bot and place them into a shopping bag. Ocado.com sells a vast range of products, running into the tens of thousands. In terms of outward physical appearance, some are much the same: a tin of chopped tomatoes, for example, is not that different from a tin of lentils. But a tin of chopped tomatoes is very different indeed from a pack of yoghurts, which in turn are more sturdy than, say, a bunch of grapes. And, of course, even grapes aren’t all the same – they vary according to their variety and state of ripeness. Get the pressure of the vacuum cup wrong and the RSP will either drop or crush the item it is attempting to manipulate. Get the sequence wrong and there’s a danger that the tin of tomatoes will squash the grapes.
At present, the company is expanding the number of items its robotic suction system can pick. “There’s no point having for 60,000 different items, 60,000 different control pieces of code,” Harvey says. “What we want at Ocado is generalised control strategies.” But challenges remain. “We need to fit a robot into the same square footage that the person sits in or operates in, and we need the robot system to achieve the same throughput.”
Until a robot can pick and pack as many items in an hour as a human being – Harvey says this is around 600–700 items – it is unlikely to be widely adopted: the impact on productivity would damage service and profits. It also has to be affordable, which means, on the one hand, scaling the technology to the point where it becomes economically worthwhile, and, on the other hand, not over-speccing it (for example, by using a camera with an unnecessarily high resolution). “When we’re deploying stuff in the real world, we want it to be economical in the way that we’re deploying it,” Harvey says. “I don’t want to deploy a supercomputer next to every robot picker.”
Whether or not such AI will ultimately replace humans is the billion-dollar question. Many now believe AI will work alongside humans. Obviously, AI offers the promise of greater efficiency, but so far, at least, this tends to hold good only where the environment is relatively controlled and predictable. Production lines and warehouses may well become fully automated. But where processes interact with the outside world – with all its randomness – it’s harder to envisage a wholly AI future. Delivery drivers, for instance, have to take into account such factors as the weather and the erratic behaviour of some pedestrians.
