  Navigation is an essential capability for mobile robots. In this paper, we
propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and
multi-stage) training framework. We optimize a mapless navigation policy with a
robust policy gradient algorithm. Our method enables different types of mobile
platforms to navigate safely in complex and highly dynamic environments, such
as pedestrian crowds. To demonstrate the superiority of our method, we test our
methods with four kinds of mobile platforms in four scenarios. Videos are
available at https://sites.google.com/view/crowdmove.
