Saenko and colleagues used reinforcement learning, or a type of machine learning based on trial and error, to train their drones. Reinforcement learning is like learning to ride a bike, where a person tries different movements until they finally find a way to pedal in a straight line. Like people, machines can try different actions and adjust as needed until their action leads to a desired result. In the case of drones, a fundamental requirement is their ability to move smoothly over long distances, and this is the problem that CAPS seeks to address.<\/p>\n The researchers added CAPS to their algorithm to ensure that their machine-trained drones respond efficiently to the quick maneuvering of pilots. CAPS helps the machine choose movements that are similar to previous movements in both space and time. Choosing similar movements allows for the drone to move around smoothly, without any jerking or jittering. Sid Mysore compares CAPS to walking. \u201cIf I\u2019ve taken a step with my right foot, then the thing I care about is if the step on my left foot is reasonable given where and when I placed down my right foot,\u201d said Mysore.<\/p>\n First, the researchers trained the machine learning algorithm in a simulated environment to ensure the drone could maneuver safely through space without putting the drone\u2019s hardware or pilot at risk. Simulated drones trained with CAPS had significantly smoother movements than those trained without CAPS.<\/p>\n Though promising, the researchers wanted to see how the drones would fly in real-world conditions. So, the team integrated their machine learning model into a drone\u2019s hardware. This allows for the drone\u2019s flight controllers to learn quickly and without human intervention. The real drone trained with CAPS moved just like the virtual drones- fluidly maneuvering through space. And, CAPS-trained drones required about 80% less power than those trained without CAPS.<\/p>\n![\"\"](\"\/hic\/files\/2021\/03\/bio-pic-636x636.jpg\")