Travel Time Dependent Maximum Entropy Inverse Reinforcement Learning for Seabird Trajectory Prediction
Trajectory prediction is a challenging problem in the fields of computer vision, robotics, and machine learning, and a number of methods for trajectory prediction have been proposed. Most methods generate trajectories that move toward a goal in a straight line (goal directed) while avoiding obstacles. However, there are not only such goal directed trajectories but also trajectories that taking detours to reach the goal (non goal directed) In this paper, we propose a method of predicting such non goal directed trajectories based on the maximum entropy inverse reinforcement learning framework. Our method introduces travel time as a state of the Markov decision process. As a practical example, we apply the proposed method to seabird trajectories measured using global positioning system loggers. Experimental results show that the proposed method can effectively predict non goal directed trajectories.
2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)