視覚言語モデルによる動的制御バリア関数の調整：安全・適応・リアルタイムな視覚ナビゲーション
Dynamic Control Barrier Function Regulation with Vision-Language Models for Safe, Adaptive, and Realtime Visual Navigation
ロボットのナビゲーションにおいて、視覚言語モデル(VLM)がカメラ画像からリスクを推定し、制御バリア関数(CBF)の安全フィルタの保守性をリアルタイムに調整するフレームワークAlphaAdjを提案。固定パラメータと比較して効率を最大18.5%向上させつつ衝突回避を維持する。
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著者: Jeffrey Chen, Rohan Chandra
分類: cs.RO
原文アブストラクト
Robots operating in dynamic, unstructured environments must balance safety and efficiency under potentially limited sensing. While control barrier functions (CBFs) provide principled collision avoidance via safety filtering, their behavior is often governed by fixed parameters that can be overly conservative in benign scenes or overly permissive near hazards. We present AlphaAdj, a vision-to-control navigation framework that uses egocentric RGB input to adapt the conservativeness of a CBF safety filter in real time. A vision-language model(VLM) produces a bounded scalar risk estimate from the current camera view, which we map to dynamically update a CBF parameter that modulates how strongly safety constraints are enforced. To address asynchronous inference and non-trivial VLM latency in practice, we combine a geometric, speed-aware dynamic cap and a staleness-gated fusion policy with lightweight implementation choices that reduce end-to-end inference overhead. We evaluate AlphaAdj across multiple static and dynamic obstacle scenarios in a variety of environments, comparing against fixed-parameter and uncapped ablations. Results show that AlphaAdj maintains collision-free navigation while improving efficiency (in terms of path length and time to goal) by up to 18.5% relative to fixed settings and improving robustness and success rate relative to an uncapped baseline.
