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 https://general-navigation-models.github.io/nomad/

翻译：在未知环境中进行机器人导航学习需要同时提供面向任务的导航策略（即到达机器人已定位的目标）和任务无关的探索策略（即在陌生场景中搜索目标）。传统方法通常采用独立模型处理这些功能，例如通过子目标提议、路径规划或分离的导航策略。本文提出了一种统一扩散策略，可同时处理目标导向导航与目标无关探索：前者实现用户指定目标的到达，后者赋予机器人在陌生环境中搜索的能力。实验表明，在视觉指引目标到达任务中，相较于基于生成模型的子目标提议方法或基于潜变量模型的传统方法，该统一策略在未知环境中展现出更优的整体性能。我们通过大规模Transformer策略实现该方法，该策略基于多台地面机器人的数据进行训练，并采用扩散模型解码器灵活处理目标条件与目标无关两种导航模式。在真实移动机器人平台上进行的实验表明，相较于五种替代方法，本方法在未知环境中实现了更有效的导航，并展现出显著的性能提升与更低的碰撞率，即使采用比现有最优方法更小的模型架构。更多视频、代码及预训练模型权重请访问：https://general-navigation-models.github.io/nomad/