Learning World Transition Model for Socially Aware Robot Navigation
Yuxiang Cui,Haodong Zhang,Yue Wang,Rong Xiong,Yuxiang Cui,Haodong Zhang,Yue Wang,Rong Xiong
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding...
