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.

And it did so on its own without a tether.

Cassie, a bipedal robot that’s all legs, has successfully run five kilometers on a single charge, all without having a tether. The machine serves as the basis for Agility Robotics’ delivery robot Digit, as TechCrunch notes, though you may also remember it for “blindly” navigating a set of stairs. Oregon State University engineers were able to train Cassie in a simulator to enable it to go up and down a flight of stairs without the use of cameras or LIDAR. Now, engineers from the same team were able to train Cassie to run using a deep reinforcement learning algorithm.

According to the team, Cassie teaching itself using the technique gave it the capability to stay upright without a tether by shifting its balance while running. The robot had to learn to make infinite subtle adjustments to be able to accomplish the feat. Yesh Godse, an undergrad from the OSU Dynamic Robotics Laboratory, explained: “Deep reinforcement learning is a powerful method in AI that opens up skills like running, skipping and walking up and down stairs.”

The team first tested Cassie’s capability by having it run on turn for five kilometers, which it finished with a time of 43 minutes and 49 seconds. Cassie finished its run across the OSU campus in 53:03. It took a bit longer because it included six and a half minutes of dealing with technical issues. The robot fell once due to a computer overheating and then again after it executed a turn too quickly. But Jeremy Dao, another team member from the lab, said they were able to “reach the limits of the hardware and show what it can do.” The work the team does will help expand the understanding of legged locomotion and could help make bipedal robots become more common in the future.