Robot foundation models hold the potential for deployment across diverse environments, from industrial applications to household tasks. While current research focuses primarily on the policies' generalization capabilities across a variety of tasks, it fails to address safety, a critical requirement for deployment on real-world systems. In this paper, we introduce a safety layer designed to constrain the action space of any generalist policy appropriately. Our approach uses ATACOM, a safe reinforcement learning algorithm that creates a safe action space and, therefore, ensures safe state transitions. By extending ATACOM to generalist policies, our method facilitates their deployment in safety-critical scenarios without requiring any specific safety fine-tuning. We demonstrate the effectiveness of this safety layer in an air hockey environment, where it prevents a puck-hitting agent from colliding with its surroundings, a failure observed in generalist policies.
View on arXiv@article{tölle2025_2503.07404,
title={ Towards Safe Robot Foundation Models },
author={ Maximilian Tölle and Theo Gruner and Daniel Palenicek and Jonas Günster and Puze Liu and Joe Watson and Davide Tateo and Jan Peters },
journal={arXiv preprint arXiv:2503.07404},
year={ 2025 }
}