安全なロボットナビゲーション基盤モデルのための制約付きデコーディング
Constrained Decoding for Safe Robot Navigation Foundation Models
自己回帰型トランスフォーマーによるロボットナビゲーション基盤モデルに対し、Signal Temporal Logicで記述された安全仕様を満たすようデコード時に制約を課す手法SafeDecを提案し、CHORESベンチマークで有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Parv Kapoor, Akila Ganlath, Michael Clifford, Changliu Liu, Sebastian Scherer, Eunsuk Kang
分類: cs.RO, cs.LG, cs.LO
原文アブストラクト
Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic systems. Trained on vast datasets of simulated and real-world trajectories, these policies map multimodal observations directly to action sequences for physical execution. Despite promising real-world capabilities, these models are still data-driven and, therefore, lack explicit notions of behavioral correctness. We address this gap by introducing SafeDec, a constrained decoding framework for autoregressive, transformer-based robot navigation foundation models that enforces safety specifications expressed as Signal Temporal Logic (STL) formulas. Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining while remaining agnostic of the underlying policy. We evaluate SafeDec on tasks from the CHORES benchmark for state-of-the-art embodied navigation policies across hundreds of procedurally generated environments and show that our decoding-time interventions are useful not only for filtering unsafe actions but also for conditional action generation. Videos are available at constrained-robot-fms.github.io
