We propose a test-time adaptation method that enables a progress estimation model to adapt online to the visual and temporal context of test trajectories by optimizing a learned self-supervised objective. To this end, we introduce a gradient-based meta-learning strategy to train the model on expert visual trajectories and their natural language task descriptions, such that test-time adaptation improves progress estimation relying on semantic content over temporal order. Our test-time adaptation method generalizes from a single training environment to diverse out-of-distribution tasks, environments, and embodiments, outperforming the state-of-the-art in-context learning approach using autoregressive vision-language models.
View on arXiv@article{ziakas2025_2506.10085,
title={ Test-Time Adaptation for Generalizable Task Progress Estimation },
author={ Christos Ziakas and Alessandra Russo },
journal={arXiv preprint arXiv:2506.10085},
year={ 2025 }
}