End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight
Robin Ferede,Christophe De Wagter,Dario Izzo,Guido C.H.E. de Croon,Robin Ferede,Christophe De Wagter,Dario Izzo,Guido C.H.E. de Croon
Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train optimal neural policies. However, a critical hurdle is the sim-to-real gap, often addressed by employing a robust inner loop controller —an abstraction that, in theory, constrains the optimality of the trained controller...
