Abstract
Image-based traversability estimation is inherently dependent on the robot platform, deployment domain, and mission preferences, which limits the applicability of purpose-trained models. To facilitate domain adaptation, this work aims to reduce the number of required annotations in the target domain using sample-efficient preference learning. Our method represents traversability through von Mises-Fisher mixture prototypes in a frozen vision-language feature space. Relative natural-language rules provide a commonsense prior, while sparse relative image annotations adapt the prototype directions and utilities to a target domain through computationally and sample-efficient fine-tuning. Experiments on WayFAST demonstrate accuracy competitive with end-to-end trained estimators while enabling sample-efficient image-based adaptation. Qualitative experiments further demonstrate the language prior's zero shot applicability and the fine-tuned estimator's improved dense prediction on semantic maps. Evaluation is complemented via semantic interpretation of learned prototypes by dissecting semantically close natural language prompts. Code and trained estimators available at https://resireg.github.io
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Schwaiger, S., Seyser, D., Scherl, A., Ajanović, Z., Wöber, W., & Steinbauer-Wagner, G. (2026). From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation. https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation
MLA 9
Schwaiger, Simon, et al. "From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation." https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation.
Chicago (author–date)
Schwaiger, Simon, David Seyser, Alessandro Scherl, Zlatan Ajanović, Wilfried Wöber, and Gerald Steinbauer-Wagner. 2026. "From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation." https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation.
Harvard
Schwaiger, S., Seyser, D., Scherl, A., Ajanović, Z., Wöber, W. and Steinbauer-Wagner, G. (2026) 'From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation', Available at: https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation.
Vancouver
Schwaiger S, Seyser D, Scherl A, Ajanović Z, Wöber W, Steinbauer-Wagner G. From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation. https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation
IEEE
S. Schwaiger, D. Seyser, A. Scherl, Z. Ajanović, W. Wöber, and G. Steinbauer-Wagner, "From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation," https://omanscience.com/en/articles/from-language-priors-to-field-adaptation-preference-learning-for-traversability-estimation.