NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
Ajay Sridhar Dhruv Shah Catherine Glossop Sergey Levine
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/