Work

Do You Know the Way? Human-in-The-Loop Understanding for Fast Traversability Estimation in Mobile Robotics

Year: 2025

Type: article

Abstract: The increasing use of robots in unstructured environments necessitates the development of effective perception and navigation strategies to enable field robots to successfully perform their tasks. In particular, it is key for such robots to understand where in their environment they can and cannot travel—a task known as traversability estimation. However, existing geometric approaches to traversability estimation may fail to capture nuanced representations of traversability, whereas vision-based approaches typically either involve manually annotating a large number of images or require robot experience. In addition, existing methods can struggle to address domain shifts as they typically do not learn during deployment. To this end, we propose a human-in-the-loop (HiL) method for traversability estimation that prompts a human for annotations as-needed. Our method uses a foundation model to enable rapid learning on new annotations and to provide accurate predictions even when trained on a small number of quickly-provided HiL annotations. We extensively validate our method in simulation and on real-world data, and demonstrate that it can provide state-of-the-art traversability prediction performance. Code is available at:https://github.com/andreschreiber/CHUNGUS.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3563819

Authors: Andre Schreiber, Katherine Driggs-Campbell

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funders: U.S. Army Corps of Engineers, Construction Engineering Research Laboratory