# Blind-Wayfarer: A Minimalist, Probing-Driven Framework for Resilient Navigation in Perception-Degraded Environments

arXiv: https://arxiv.org/abs/2503.07492 · HTML: https://mlsanity.com/paper/2503.07492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2605.31481/) (2026)
- [GAFT: Geo-Anchored Fine-Tuning for Hazard Identification from Rare Failures](https://mlsanity.com/paper/2608.30858/) (2026)

## Abstract (arXiv)

Navigating autonomous robots through dense forests and rugged terrains is especially daunting when exteroceptive sensors -- such as cameras and LiDAR sensors -- fail under occlusions, low-light conditions, or sensor noise. We present Blind-Wayfarer, a probing-driven navigation framework inspired by maze-solving algorithms that relies primarily on a compass to robustly traverse complex, unstructured environments. In 1,000 simulated forest experiments, Blind-Wayfarer achieved a 99.7% success rate. In real-world tests in two distinct scenarios -- with rover platforms of different sizes -- our approach successfully escaped forest entrapments in all 20 trials. Remarkably, our framework also enabled a robot to escape a dense woodland, traveling from 45 m inside the forest to a paved pathway at its edge. These findings highlight the potential of probing-based methods for reliable navigation in challenging perception-degraded field conditions. Videos and code are available on our website https://sites.google.com/view/blind-wayfarer