Adam Coates for acrobatic flight maneuvers of the Stanford autonomous helicopter. Autonomous helicopter flight is widely regarded to be a highly challenging control problem. It is particularly difficult to design controllers for non-stationary maneuvers in which the helicopter goes through various flight regimes, extensively exposing the great complexity of helicopter dynamics. Despite these challenges, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter's capabilities.

Pieter Abbeel and Adam Coates developed apprenticeship learning algorithms that leverage expert demonstrations to efficiently learn good controllers for the tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled their helicopters to significantly extend the state of the art in autonomous helicopter flight and aerobatics. Their experimental results included the first autonomous execution of a wide range of maneuvers, including flips, rolls, loops, auto-rotation landings, chaos and tictocs, which only exceptional human pilots can perform. Their results also included complete air shows, which required autonomous transitions between many of these maneuvers. Their system performs as well, and often even better, than an expert human pilot.