# SyNeT: Synthetic Negatives for Traversability Learning

arXiv: https://arxiv.org/abs/2602.00814 · HTML: https://mlsanity.com/paper/2602.00814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models](https://mlsanity.com/paper/2412.07679/) (2024)
- [Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning](https://mlsanity.com/paper/1912.03227/) (2019)
- [V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation](https://mlsanity.com/paper/2312.16016/) (2023)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments](https://mlsanity.com/paper/2209.06522/) (2022)
- [Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models](https://mlsanity.com/paper/2306.15226/) (2023)
- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [CEAR: Comprehensive Event Camera Dataset for Rapid Perception of Agile Quadruped Robots](https://mlsanity.com/paper/2404.04698/) (2024)
- [Follow the Footprints: Self-supervised Traversability Estimation for Off-road Vehicle Navigation based on Geometric and Visual Cues](https://mlsanity.com/paper/2402.15363/) (2024)
- [Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy](https://mlsanity.com/paper/2406.02989/) (2024)
- [Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance](https://mlsanity.com/paper/2211.11201/) (2022)
- [W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics](https://mlsanity.com/paper/2406.02822/) (2024)
- [IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning](https://mlsanity.com/paper/2409.14070/) (2024)
- [Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features](https://mlsanity.com/paper/2501.17594/) (2025)
- [Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17479/) (2024)
- [Do You Know the Way? Human-in-the-Loop Understanding for Fast Traversability Estimation in Mobile Robotics](https://mlsanity.com/paper/2504.19851/) (2025)
- [Self-Supervised Traversability Learning with Online Prototype Adaptation for Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.12109/) (2025)

## Abstract (arXiv)

Reliable traversability estimation is crucial for autonomous robots to navigate complex outdoor environments safely. Existing self-supervised learning frameworks primarily rely on positive and unlabeled data; however, the lack of explicit negative data remains a critical limitation, hindering the model's ability to accurately identify diverse non-traversable regions. To address this issue, we introduce a method to explicitly construct synthetic negatives, representing plausible but non-traversable, and integrate them into vision-based traversability learning. Our approach is formulated as a training strategy that can be seamlessly integrated into both Positive-Unlabeled (PU) and Positive-Negative (PN) frameworks without modifying inference architectures. Complementing standard pixel-wise metrics, we introduce an object-centric FPR evaluation approach that analyzes predictions in regions where synthetic negatives are inserted. This evaluation provides an indirect measure of the model's ability to consistently identify non-traversable regions without additional manual labeling. Extensive experiments on both public and self-collected datasets demonstrate that our approach significantly enhances robustness and generalization across diverse environments. The source code and demonstration videos will be publicly available.