Robots today have been programmed to vacuum the floor or perform a preset dance, but there is still much work to be done before they can achieve their full potential. This mainly has something to do with how robots are unable to recognize what is in their environment at a deep level and therefore cannot function properly without being told all of these details by humans. For instance, while it may seem like backup programming for when bumping into an object that would help prevent unwanted collisions from happening again, this idea isn’t actually based on understanding anything about chairs because the robot doesn’t know exactly what one is!

Facebook AI team just released Droidlet, a new platform that makes it easier for anyone to build their smart robot. It’s an open-source project explicitly designed with hobbyists and researchers in mind so you can quickly prototype your AI algorithms without having to spend countless hours coding everything from scratch.

Droidlet is a platform for building embodied agents capable of recognizing, reacting to, and navigating the world. It simplifies integrating all kinds of state-of-the-art machine learning algorithms in these systems so that users can prototype new ideas faster than ever before!

Granted, it’s a little different for a robot, since they don’t have lungs or a heart. But they do have a “brain” (software), “muscles” (hardware), and “fuel” (a battery), and these all had to work together for Cassie to be able to run.

The brunt of the work fell to the brain—in this case, a machine learning algorithm developed by students at Oregon State University’s Dynamic Robotics Laboratory. Specifically, they used deep reinforcement learning, a method that mimics the way humans learn from experience by using a trial-and-error process guided by feedback and rewards. Over many repetitions, the algorithm uses this process to learn how to accomplish a set task. In this case, since it was trying to learn to run, it may have tried moving the robot’s legs varying distances or at distinct angles while keeping it upright.

Once Cassie got a good gait down, completing the 5K was as much a matter of battery life as running prowess. The robot covered the whole distance (a course circling around the university campus) on a single battery charge in just over 53 minutes, but that did include six and a half minutes of troubleshooting; the computer had to be reset after it overheated, as well as after Cassie fell during a high-speed turn. But hey, an overheated computer getting reset isn’t so different from a human runner pausing to douse their head and face with a cup of water to cool off, or chug some water to rehydrate.