# SALON: Self-supervised Adaptive Learning for Off-road Navigation

arXiv: https://arxiv.org/abs/2412.07826 · HTML: https://mlsanity.com/paper/2412.07826/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 6 of 6)

- [WildOS: Open-Vocabulary Object Search in the Wild](https://mlsanity.com/paper/2602.19308/) (2026)
- [TADPO: Reinforcement Learning Goes Off-road](https://mlsanity.com/paper/2603.05995/) (2026)
- [AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2507.17957/) (2025)
- [Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation](https://mlsanity.com/paper/2511.19647/) (2025)
- [Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments](https://mlsanity.com/paper/2605.28442/) (2026)
- [Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning](https://mlsanity.com/paper/2605.31119/) (2026)

## Abstract (arXiv)

Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. Our code is available at https://theairlab.org/SALON.