Inverse reinforcement learning control for trajectory tracking of a multirotor UAV

The main purpose of this paper is to learn the control performance of an expert by imitating the demonstrations of a multirotor UAV (unmanned aerial vehicle) operated by an expert pilot. First, we collect a set of several demonstrations by an expert for a certain task which we want to learn. We extract a representative trajectory from the dataset. Here, the representative trajectory includes a sequence of state and input. The trajectory is obtained using hidden Markov model (HMM) and dynamic time warping (DTW). In the next step, the multirotor learns to track the trajectory for imitation. Although we have data of feed-forward input for each time sequence, using this input directly can deteriorate the stability of the multirotor due to insufficient data for generalization and numerical issues. For that reason, a controller is needed which generates the input command for the suitable flight maneuver. To design such a controller, we learn the hidden reward function of a quadratic form from the demonstrated flights using inverse reinforcement learning. After we find the optimal reward function that minimizes the trajectory tracking error, we design a reinforcement learning based controller using this reward function. The simulation and experiment applied to a multirotor UAV show successful imitation results.