Paper published in Science Robotics and featured on the cover
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning was published in Science Robotics and featured on the journal’s cover. The paper presents a systematic study of control system design methodologies, focusing on reinforcement learning and optimal control in the context of autonomous drone racing. We show that neural networks trained with reinforcement learning (RL) outperform optimal control methods and trace the root cause to the flexibility afforded by RL in the formulation of the controller’s objective. In conjunction with the study, we push autonomous drones to unprecedented performance regimes, demonstrating superhuman control while reaching accelerations greater than 12g and velocities greater than 100 km/h.
