Reinforcement learning with data bootstrapping for dynamic subgoal pursuit in humanoid robot navigation

This work combines a reinforcement learning planner for dynamic subgoal selection with a model predictive control gait planner. Training uses navigation data from a model-based method to bootstrap learning. The authors evaluate the framework in Digit simulation with randomly placed obstacles.