Depictions of the future in books and film are usually influenced by what’s going on at the time, reflecting social malaise, impending armageddon, or economic anxieties. The robots in classic sci-fi usually resembled humans, as most authors assumed they would eventually assist us in the same tasks humans did.
Instead, however, today we find artificial intelligence doing some of our thinking for us, but it’s more often solving problems that don’t need intervention from self-contained humanoid robots. The promise of autonomous robots that matched our abilities has given way to a more specific focus on tasks that are fulfilled by armies of smaller bots controlled by machine learning and algorithms running in the cloud. Their scope is more complex, but a lot less dramatic than, say, Forbidden Planet’s Robby the Robot, or the replicants from Blade Runner.
Not so long agoTechnica looked at the uses of robotics in retail and the range of different approaches, from wheeling shelves around giant Amazon warehouses, to Domino’s forthcoming airborne pizza drones. The response to that story was very positive, so we decided that we would follow up with another piece that dives even deeper into the world of automation. The first story focused on what is being done with automation; now we’re going to look at how you build an automated system.
Table of Contents
- Why automate?
- Optimisation in 4D
- Building the robots
- Building a hive mind
- In silico
- Robots must be resilient
The assumption is that, despite the large amount of effort and cost of designing and deploying an automated system, the end result will be worth it—that the automated system does the job better and cheaper than before.
Automation is faster than the human alternative. Even if a bot is slower in absolute terms, it doesn’t suffer from fatigue, makes fewer mistakes, and can work 24/7 to make up for any tardiness. Furthermore, unlike hard automation, robots are adaptable and can be taught new tricks.
This sounds satisfactory enough, but off-the-shelf robotics will inevitably present shortcomings for those who truly want to exploit magnificent machine-learning algorithms in real-world settings. Factors such as a robot’s physical design may be less than ideal for a specific environment, or the sensing and control mechanism might not be suitable for the intended workflow.
The choices faced by any big firm that wants to go robotic, or indeed a solo inventor who wants to craft the first Cylon, are stark: redesign an existing system to try and suit your needs, or build a new one yourself. Customising someone else’s robots is cheaper, but it’s necessarily a compromise and, done poorly, could totally undermine the advantages and efficiencies that come from automation.
So just how hard is it to build an army of robots from the ground up?
That’s a good question to put to Ocado’s CTO Paul Clarke, whose division Ocado Technology, in partnership with Ocado Engineering, is developing robots that will one day allow for fully autonomous warehouses.was invited to take a closer, exclusive look at the company’s homegrown robots that are currently live-trialling at its new Andover facility.
Robots, Clarke explains, are one part of a grocery retail enterprise that manages incoming goods in warehouses known as “customer fulfilment centres” (CFCs), and ferries them around to human pickers, who process orders by placing items in separate baskets to make up customer orders. Customer baskets then end up in a van and are put out for delivery.
One day the human basket-fillers will be joined by robots equipped with compliant, human-like grippers (Ocado is involved with the EU’s SoMa project), and some of the vans may be replaced with autonomous delivery vehicles, and all of these robots will be tended to by humanoid SecondHands robots that wander the CFC floor looking for machines in need of maintenance… but we’re not quite there yet.
For now, the focus is on building a hive of thousands of custom-built robots that bring products to the humans, optimising their behaviour, and reliably communicating with them so that they don’t have another Rogue Robot Situation on their hands.
But we’re getting ahead of ourselves. Before you raise an army, particularly a robotic one, you need to define the army’s operational theatre. Furthermore, you have to work out how the army will behave within that arena and how it will physically go about achieving its goals. To do all that, the first step is collecting and analysing a lot of data.