Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation

TL;DR

This paper introduces a reinforcement learning-based local navigation policy conditioned on path information, improving long-range navigation efficiency and robustness to degraded guidance in uncertain environments.

Contribution

It proposes a novel path-conditioned RL policy that adapts to varying path quality, enhancing long-range navigation robustness and efficiency.

Findings

Significantly improves navigation efficiency with high-quality paths.

Maintains baseline performance when path guidance is degraded.

Robust to misleading or absent path information.

Abstract

Long-range navigation is commonly addressed through hierarchical pipelines in which a global planner generates a path, decomposed into waypoints, and followed sequentially by a local planner. These systems are sensitive to global path quality, as inaccurate remote sensing data can result in locally infeasible waypoints, which degrade local execution. At the same time, the limited global context available to the local planner hinders long-range efficiency. To address this issue, we propose a reinforcement learning-based local navigation policy that leverages path information as contextual guidance. The policy is conditioned on reference path observations and trained with a reward function mainly based on goal-reaching objectives, without any explicit path-following reward. Through this implicit conditioning, the policy learns to opportunistically exploit path information while remaining robust to misleading or degraded guidance. Experimental results show that the proposed approach significantly improves navigation efficiency when high-quality paths are available and maintains baseline-level performance when path observations are severely degraded or even non-existent. These properties make the method particularly well-suited for long-range navigation scenarios in which high-level plans are approximate and local execution must remain adaptive to uncertainty.