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I’ll be back … and maybe not where you expect

By Chris Roberts - Last updated: Monday, July 20, 2015

Last month I gave a talk about some of the work we’re doing in machine vision and robotics. See presentation at the bottom of this blog. After giving this talk, one thing really stands out – the public perception of robots is often very different from the reality. Compare the way they’re presented in fiction – e.g Terminator’s killer cyborgs, or the domestic servants in the Channel 4 series Humans – with the tasks they can currently achieve. It’s an area of engineering where it can be difficult to determine which problems are easy, and which are hard. Robots in car production lines, for example, can move metal parts weighing hundreds of kilograms from one place to another with sub-millimetre accuracy – but this is simple for the robots and the computers driving them to do because the parts are all identical and the positions never change. The winners of the Amazon Picking Challenge, on the other hand, managed to pick 12 objects in 20 minutes. This might be state of the art when it comes to robots, but it’s a long way from what a human can do, let alone the Terminator…

The Amazon Picking Challenge was a very hard problem for a robot and computer vision systems to solve, it’s no wonder they did so badly compared to humans. However, this doesn’t mean there’s no scope for widening the class of problems that they can tackle. Our recent work in this area suggests that the key to success is to choose the scope carefully – aim for a system that’s flexible enough to significantly increase productivity, but not so flexible that it can’t do anything quickly. Another thing we’ve learnt is that development of these types of system is expensive, so isn’t worth doing without a business case in place. The proof of concept stage isn’t the hard one – it’s relatively straightforward to build something out of off the shelf robotics and cobbled together Matlab that runs a new image processing algorithm… slowly. The hard part is the engineering, not the science – making the whole system run as fast as a human would, or faster, safely and robustly. And that requires big teams of expensive, experienced engineers – it’s not going to arise spontaneously from hobbyists or university teams working on a Raspberry Pi.

But – we are starting to see business cases where the rewards for innovation in automation are so great that it’s worth investing large sums in. So don’t be surprised if robots start popping up in unexpected places in the near future – we’re not going to see androids walking the streets in my lifetime, but they do have the potential to transform a whole new set of industrial and commercial processes.

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AuthorChris Roberts

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