Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps
Matt Schmittle, Rohan Baijal, Nathan Hatch, Rosario Scalise, Mateo Guaman Castro, Sidharth Talia, Khimya Khetarpal, Byron Boots
Abstract
A robot navigating an outdoor environment with no prior knowledge of the space must rely on its local sensing, which is in the form of a local metric map or local policy with some fixed horizon. A limited planning horizon can often result in myopic decisions leading the robot off course or worse, into very difficult terrain. In this work, we make a key observation that long range navigation only necessitates identifying good frontier directions for planning instead of full map knowledge. To address this, we introduce Long Range Navigator (LRN), which learns to predict ‘affordable’ frontier directions from high-dimensional camera images. LRN is trained entirely on unlabeled egocentric videos, making it scalable and adaptable. In off-road tests on Spot and a large vehicle, LRN reduces human interventions and improves decision speed when integrated into existing navigation stacks.
BibTeX
@inproceedings{
schmittle2025long,
title={Long Range Navigator ({LRN}): Extending robot planning horizons beyond metric maps},
author={Matt Schmittle and Rohan Baijal and Nathan Hatch and Rosario Scalise and Mateo Guaman Castro and Sidharth Talia and Khimya Khetarpal and Byron Boots and Siddhartha Srinivasa},
booktitle={9th Annual Conference on Robot Learning},
year={2025},
url={https://openreview.net/forum?id=QtVZUPCKrY}
}