We here at Skydio have been developing and deploying machine learning systems for years due to their ability to scale and improve with data. However, to date our learning systems have only been used for interpreting information about the world; in this post, we present our first machine learning system for actually acting in the world.
Using a novel learning algorithm, the Skydio autonomy engine, and only 3 hours of “off-policy” logged data, we trained a deep neural network pilot that is capable of filming and tracking a subject while avoiding obstacles.
We approached the problem of training a deep neural network pilot through the lens of imitation learning, in which the goal is to train a model that imitates an expert. Imitation learning was an appealing approach for us because we have a huge trove of flight data with an excellent drone pilot—the motion planner inside the Skydio autonomy engine. However, we quickly found that standard imitation learning performed poorly when applied to our challenging, real-world problem domain.
Standard imitation learning worked fine in easy scenarios, but did not generalize well to difficult ones. We propose that the signal of the expert’s trajectory is not rich enough to learn efficiently. Especially within our domain of flying through the air, the exact choice of flight path is a weak signal because there can be many obstacle-free paths that lead to cinematic video. The average scenario overwhelms the training signal. [READ MORE]