Robotic learning for navigation in unfamiliar environments needs to provide
policies for both task-oriented navigation (i.e., reaching a goal that the
robot has located), and task-agnostic exploration (i.e., searching for a goal
in a novel setting). Typically, these roles are handled by separate models, for
example by using subgoal proposals, planning, or separate navigation
strategies. In this paper, we describe how we can train a single unified
diffusion policy to handle both goal-directed navigation and goal-agnostic
exploration, with the latter providing the ability to search novel
environments, and the former providing the ability to reach a user-specified
goal once it has been located. We show that this unified policy results in
better overall performance when navigating to visually indicated goals in novel
environments, as compared to approaches that use subgoal proposals from
generative models, or prior methods based on latent variable models. We
instantiate our method by using a large-scale Transformer-based policy trained
on data from multiple ground robots, with a diffusion model decoder to flexibly
handle both goal-conditioned and goal-agnostic navigation. Our experiments,
conducted on a real-world mobile robot platform, show effective navigation in
unseen environments in comparison with five alternative methods, and
demonstrate significant improvements in performance and lower collision rates,
despite utilizing smaller models than state-of-the-art approaches. For more
videos, code, and pre-trained model checkpoints, see
this https URL

机器人学习在陌生环境中导航需要提供任务导向导航和任务无关探索的策略。本文介绍了如何训练一个统一的扩散策略来处理目标导向导航和目标无关探索，证明了该统一策略在导航到视觉指示目标时相比于其他方法具有更好的性能和更低的碰撞率。