General Navigation Models are general-purpose goal-conditioned visual navigation policies trained on diverse, cross-embodiment training data, and can control many different robots in zero-shot. They can also be efficiently fine-tuned, or adapted, to new robots and downstream tasks.


ViNT: A Foundation Model for Visual Navigation

Oral Talk at Conference on Robot Learning (CoRL) 2023
Atlanta, Georgia

Live Demo at Conference on Robot Learning (CoRL) 2023
Live Demo at Robot Learning Workshop, NeurIPS 2023
Oral Talk at Bay Area Machine Learning Symposium (BayLearn) 2023

NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration

Best Paper Award Winner at ICRA 2024
Yokohama, Japan

Best Student Paper Award Finalist at ICRA 2024
Best Paper Award in Cognitive Robotics Finalist at ICRA 2024
Oral Talk at NeurIPS Workshop on Foundation Models for Decision Making 2023
Oral Talk at CoRL Workshop on Pre-Training for Robot Learning 2023

General Navigation Models in-the-wild

GNM, ViNT, and NoMaD are available as ready-to-deploy navigation policies, and have already been used by researchers around the world on their own robots and environments.

Used GNM-family of models in your research? Send us a video of your robot in action , and we'll add it below! We love to see our models in the wild.

The website (source code) design was adapted from Nerfies.