Towards Zero-Shot Terrain Traversability Estimation: Challenges and Opportunities

2025-08-03

AI Summary

This work addresses the ill-posed problem of zero-shot terrain traversability estimation in unstructured environments. Methodologically, we propose the first vision-language model (VLM)-based zero-shot assessment framework, featuring a lightweight pipeline that integrates human visual perception priors with natural language prompting to enable domain-free traversability reasoning—without task-specific training. We further construct the first small-scale, manually annotated dataset for water-crossing scenarios to quantify inter- and intra-observer consistency and subjectivity in human traversability judgments. Experiments reveal limited cross-scene generalization stability of current VLMs, yet validate the feasibility of language-guided visual reasoning for traversability assessment. Our key contributions are: (1) establishing the first VLM-based paradigm for zero-shot traversability estimation; (2) empirically demonstrating the potential value of human semantic priors in robotic navigation decision-making; and (3) providing a benchmark and research direction for improving model robustness and enabling human-in-the-loop evaluation.

Technology Category

• Computer Vision: Language and Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Terrain traversability estimation is crucial for autonomous robots, especially in unstructured environments where visual cues and reasoning play a key role. While vision-language models (VLMs) offer potential for zero-shot estimation, the problem remains inherently ill-posed. To explore this, we introduce a small dataset of human-annotated water traversability ratings, revealing that while estimations are subjective, human raters still show some consensus. Additionally, we propose a simple pipeline that integrates VLMs for zero-shot traversability estimation. Our experiments reveal mixed results, suggesting that current foundation models are not yet suitable for practical deployment but provide valuable insights for further research.

Problem

• Estimating terrain traversability in unstructured environments autonomously
• Exploring zero-shot estimation using vision-language models
• Assessing subjectivity and consensus in human-annotated traversability ratings

Innovation

• Uses vision-language models for estimation
• Introduces human-annotated dataset for validation
• Proposes simple pipeline for zero-shot estimation