Towards General Language-Conditioned Latent Safety Filters
arXiv:2608.00315v1 Announce Type: new Abstract: Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language. Safe deployment, however, requires adapting not only to new tasks but also to varying safety requirements across users, environments, and applications. Existing safety filters remain largely constraint-specific and thus must be redesigned or relearned when safety requirements change. In this paper, we investigate language-conditioned safety filtering, in which a Hamilton-Jacobi safety actor and critic are conditioned on language-specified constraints. We evaluate this formulation across pick-and-place, table-wiping, and block-stacking tasks in the vision-based setting, examining its ability to enforce language-specified constraints and transfer to unseen constraint instances within the evaluated constraint families. Our experiments provide evidence that language-conditioned safety filters reduce constraint violations and exhibit partial transfer to unseen constraint instances.
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[Submitted on 31 Jul 2026]
Title:Towards General Language-Conditioned Latent Safety Filters
View a PDF of the paper titled Towards General Language-Conditioned Latent Safety Filters, by Ihab Tabbara and Yuxuan Yang and Hussein Sibai
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Abstract:Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language. Safe deployment, however, requires adapting not only to new tasks but also to varying safety requirements across users, environments, and applications. Existing safety filters remain largely constraint-specific and thus must be redesigned or relearned when safety requirements change. In this paper, we investigate language-conditioned safety filtering, in which a Hamilton-Jacobi safety actor and critic are conditioned on language-specified constraints. We evaluate this formulation across pick-and-place, table-wiping, and block-stacking tasks in the vision-based setting, examining its ability to enforce language-specified constraints and transfer to unseen constraint instances within the evaluated constraint families. Our experiments provide evidence that language-conditioned safety filters reduce constraint violations and exhibit partial transfer to unseen constraint instances.
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2608.00315 [cs.RO]
(or arXiv:2608.00315v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.00315
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ihab Tabbara [view email] [v1] Fri, 31 Jul 2026 22:00:35 UTC (8,556 KB)
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