NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration

By Ajay Kumar Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine. IEEE International Conference on Robotics and Automation, 2023

373 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/05bad81069c8b11202e90ac16c543efc774e2800

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Citations in the corpus

Octo: An Open-Source Generalist Robot Policy (2024, methodology): “…in robot imitation learning and scalable transformer training, including the use of denoising diffusion objectives [34] for action decoding [17, 31, 85], the prediction of “action chunks”, i.e., sequences of future actions [98, 17, 28], and model layouts and learning rate schedules inspired by the…”
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (2024, positive): “Additionally, recent works suggest that diffusion denoising models [22] are a powerful parametrization for multi-modal action output distributions that combine expressivity with scalability [7, 16, 21, 38, 54].”
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving (2024, positive): “Subsequently, numerous works have applied diffusion models to various robotic tasks, including stationary manipulation [1, 53], mobile manipulation [47], autonomous nav-12039 igation [37, 51], quadruped locomotion [38], and dexterous manipulation [46].”