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

arXiv: https://arxiv.org/abs/2603.13888 · HTML: https://mlsanity.com/paper/2603.13888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 20 of 20)

- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints](https://mlsanity.com/paper/2202.11271/) (2022)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [A Comprehensive Review on Autonomous Navigation](https://mlsanity.com/paper/2212.12808/) (2022)
- [Deep Local Trajectory Replanning and Control for Robot Navigation](https://mlsanity.com/paper/1905.05279/) (2019)
- [IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience](https://mlsanity.com/paper/2305.01098/) (2023)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios](https://mlsanity.com/paper/1807.07870/) (2018)
- [Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks](https://mlsanity.com/paper/2107.06661/) (2021)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics](https://mlsanity.com/paper/2111.07908/) (2021)
- [Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment](https://mlsanity.com/paper/2404.14157/) (2024)
- [Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle](https://mlsanity.com/paper/2401.02903/) (2024)
- [Large-Scale Autonomous Gas Monitoring for Volcanic Environments: A Legged Robot on Mount Etna](https://mlsanity.com/paper/2601.07362/) (2026)
- [FocusNav: Spatial Selective Attention with Waypoint Guidance for Humanoid Local Navigation](https://mlsanity.com/paper/2601.12790/) (2026)

## Abstract (arXiv)

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.