# W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics

arXiv: https://arxiv.org/abs/2406.02822 · HTML: https://mlsanity.com/paper/2406.02822/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [SyNeT: Synthetic Negatives for Traversability Learning](https://mlsanity.com/paper/2602.00814/) (2026)
- [Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking](https://mlsanity.com/paper/2609.23673/) (2026)
- [From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation](https://mlsanity.com/paper/2610.02974/) (2026)

## Abstract (arXiv)

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation--which predicts where in the environment a robot can travel--is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments.