Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification or segmentation. Inspired by this, we introduce Theia, a vision foundation model for robot learning that distills multiple off-the-shelf vision foundation models trained on varied vision tasks. Theia's rich visual representations encode diverse visual knowledge, enhancing downstream robot learning. Extensive experiments demonstrate that Theia outperforms its teacher models and prior robot learning models using less training data and smaller model sizes. Additionally, we quantify the quality of pre-trained visual representations and hypothesize that higher entropy in feature norm distributions leads to improved robot learning performance. Code, models, and demo are available at https://theia.theaiinstitute.com.
@article{arxiv.2407.20179,
title = {Theia: Distilling Diverse Vision Foundation Models for Robot Learning},
author = {Jinghuan Shang and Karl Schmeckpeper and Brandon B. May and Maria Vittoria Minniti and Tarik Kelestemur and David Watkins and Laura Herlant},
journal= {arXiv preprint arXiv:2407.20179},
year = {2024}
}