If your robotics lab has a quadruped, it’s become almost a requirement that you post a video of the robot not falling over when walking across some kind of particularly challenging surface. And quadrupeds are getting quite good at keeping their feet, even while negotiating uneven terrain like steps or rubble. One way to do this is without any visual perception at all, simply reacting to obstacles “blindly” by positioning legs and feet to keep the body of the robot upright and moving in the right direction. This can work for terrain that’s continuous, but when you start looking at more dangerous situations like gaps that a robot’s leg could get stuck in, being able to use vision to plan a safe path becomes necessary.

Vision, though, is a real bag of worms, kettle of fish, bushel of geese, or whatever your own favorite tricky metaphor is. Adapting foot placement based on visual feedback takes both reliable sensing and the processing power to back it up, but even under the best of circumstances, there’s only so much that an onboard system can usually handle. At the Italian Institute of Technology, roboticists have used a convolutional neural network to reduce the time that it takes for the HyQ quadruped to plan its foot placement by several orders of magnitude, and it can now make dynamic adaptations, allowing it to withstand an extra helping of abuse from its human programmers.