汎用言語条件付き潜在安全フィルタに向けて
Towards General Language-Conditioned Latent Safety Filters
言語で指定された安全制約を満たすように、ハミルトン-ヤコビ安全アクターと批評家を条件付けする安全フィルタを提案し、ピックアンドプレースやテーブル拭きなどのタスクで制約違反を減らし、未見の制約への部分的な転移を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Ihab Tabbara, Yuxuan Yang, Hussein Sibai
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
