家庭用ロボットナビゲーションにおける障害物条件付き安全性のベンチマーク
Benchmarking Obstacle-Conditioned Safety in Household Robot Navigation
障害物の種類だけを変えた空間的に一致したシナリオで家庭内ナビゲーションの安全性を評価するOSMA-Benchと、障害物の危険度順序に沿って速度や余裕を調整できるかを測るSemantic Sensitivity Indexを提案し、VLM/VLAポリシーが意味的な安全区別を運動に反映できていないことを示した。
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: Hyunwoong Bae, Jisu Han, Jiyeon Choi, Dohoon Kim, Taesup Moon
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Safe household navigation requires robots to regulate clearance and speed according to what an obstacle is and how close it is, without unnecessary conservatism. Yet existing evaluations do not test whether semantic safety distinctions are reflected in continuous motion. To that end, we introduce OSMA-Bench, an Obstacle-Conditioned Safe Motion Adaptation Benchmark built on spatially matched scenario sets that differ only in obstacle type. We also propose the \emph{Semantic Sensitivity Index} (SSI), which measures whether clearance and distance-dependent speed and smoothness follow the caution ordering of obstacles. Evaluating VLM-based and vision-language-action (VLA) policies on the benchmark, we find that neither consistently adapts its motion to obstacle safety, even with explicit safety prompts. Our analysis shows that the policies can correctly distinguish obstacles by safety relevance but fail to reflect this distinction in the motion parameters or actions they produce. For VLM-based policies, which recognize the obstacle and then generate a navigation program, adding an explicit parameter-inference step between these two stages improves adaptation. These findings expose a gap in \emph{semantic-to-motion grounding}, highlighting the need to translate semantic caution into appropriately safe motion.