Robots are tremendously useful machines, and I would like to see them applied to every task where they can do some good. Yet we don’t have enough programmers for all this hardware and all these tasks. To be useful, robots need to be intelligent enough to learn from experience in the real world and communicate what they’ve learned for the benefit of other robots. I hope that the coming year will see great progress in this area.

Unlike many typical machine learning systems, robots need to be highly reliable. If you’re using a face detection system to find pictures of friends in an image library, it’s not much of a problem if the system fails to find a particular face or finds an incorrect one. But mistakes can be very costly when physical systems interact with the real world. Consider a warehouse robot that surveys shelves full of items, identifies the ones that a customer has paid for, grasps them, and puts them in a box. (Never mind an autonomous car that could cause a crash if it makes a mistake!) Whether this robot classifies objects accurately isn’t a matter of life and death, but even if its classification accuracy is 99.9 percent, one in 1,000 customers will receive the wrong item.

After decades of programming robots to act according to rules tailored for specific situations, roboticists now embrace machine learning as the most promising path to building machines that achieve human performance in tasks like the warehouse robot’s pick-and-place. Deep learning provides excellent visual perception including object recognition and semantic segmentation. Meanwhile, reinforcement learning offers a way to learn virtually any task. Together, these techniques offer the most promising path to harnessing robots everywhere they would be useful in the real world.

What’s missing from this recipe? The real world itself. We train visual systems on standardized datasets, and we train robot behaviors in simulated environments. Even when we don’t use simulations, we keep robots cooped up in labs. There are good reasons to do this: Benchmark datasets represent a useful data distribution for training and testing on particular tasks, simulations allow dirt-cheap virtual robots to undertake immense numbers of learning trials in relatively little time, and keeping robots in the lab protects them — and nearby humans — from potentially costly damage.

But it is becoming clear that neither datasets nor simulations are sufficient. Benchmark tasks are more tightly defined than many real-world applications, and simulations and labs are far simpler than real-world environments. Progress will come more rapidly as we get better at training physical robots in the real world.

To do this, we can’t treat robots as solitary learners that bumble their way through novel situations one at a time. They need to be part of a class, so they can inform one another. This fleet-learning concept can unite thousands of robots, all learning on their own and from one another by sharing their perceptions, actions, and outcomes. We don’t yet know how to accomplish this, but important work in lifelong learning and incremental learning provides a foundation for robots to gain real-word experience quickly and cost-effectively. Then they can sharpen their knowledge in simulations and take what they learn in simulations back to the real world in a loop that takes advantage of the strengths of each environment.

In the coming year, I hope that roboticists will shut down their sims, buy physical robots, take them out of the lab, and start training them on practical tasks in real-world settings. Let’s try this for a year and see how far we get!

Wolfram Burgard is a professor at the University of Freiburg, where he heads the Autonomous Intelligent Systems research lab.


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