Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
A robot navigating an outdoor environment with no prior knowledge of the
space must rely on its local sensing to perceive its surroundings and plan.
This can come in the form of a local metric map or local policy with some fixed
horizon. Beyond that, there is a fog of unknown space marked with some fixed
cost. A limited planning horizon can often result in myopic decisions leading
the robot off course or worse, into very difficult terrain. Ideally, we would
like the robot to have full knowledge that can be orders of magnitude larger
than a local cost map. In practice, this is intractable due to sparse sensing
information and often computationally expensive. 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 this end, we
propose Long Range Navigator (LRN), that learns an intermediate affordance
representation mapping high-dimensional camera images to `affordable' frontiers
for planning, and then optimizing for maximum alignment with the desired goal.
LRN notably is trained entirely on unlabeled ego-centric videos making it easy
to scale and adapt to new platforms. Through extensive off-road experiments on
Spot and a Big Vehicle, we find that augmenting existing navigation stacks with
LRN reduces human interventions at test-time and leads to faster decision
making indicating the relevance of LRN. https://personalrobotics.github.io/lrn
