Code is coming soon. This repository will host the official implementation of NavOL, an online imitation learning framework for visual navigation built on IsaacLab and a pre-trained navigation diffusion policy.
🌐 https://logosroboticsgroup.github.io/NavOL/
NavOL: Navigation Policy with Online Imitation Learning
Xiaofei Wei*, Chun Gu*, Li Zhang
School of Data Science, Fudan University · Shanghai Innovation Institute
ICML 2026 (Accepted)
NavOL fine-tunes a pre-trained navigation diffusion policy (NavDP) via massively parallel rollouts in IsaacLab, supervised online by a privileged global path planner. The rollout–update loop trains on the policy's own visited state distribution, removing the need for reward design and mitigating distribution shift in offline imitation learning. With 8 RTX 4090 GPUs, the system collects more than 2,000 high-quality trajectories per hour across 50 indoor 3D scenes.
- Training code (rollout–update loop on top of IsaacLab + NavDP)
- Indoor navigation benchmark on 3D-Front
- Pre-trained checkpoints
- Real-world deployment scripts
@inproceedings{wei2026navol,
title = {{NavOL}: Navigation Policy with Online Imitation Learning},
author = {Wei, Xiaofei and Gu, Chun and Zhang, Li},
booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
year = {2026}
}