If you’ve watched Boston Dynamics’ slick videos of robots running, jumping and doing parkour, you might have the impression robots have learned to be amazingly agile. In fact, these robots are still coded by hand, and would struggle to deal with new obstacles they haven’t encountered before.

However, a new method of teaching robots to move could help to deal with new scenarios, through trial and error—just as humans learn and adapt to unpredictable events.  

Researchers used an AI technique called reinforcement learning to help a two-legged robot nicknamed Cassie to run 400 meters, over varying terrains, and execute standing long jumps and high jumps, without being trained explicitly on each movement. Reinforcement learning works by rewarding or penalizing an AI as it tries to carry out an objective. In this case, the approach taught the robot to generalize and respond in new scenarios, instead of freezing like its predecessors may have done.