安全机器人导航基础模型的约束解码
Constrained Decoding for Safe Robot Navigation Foundation Models
AI总结：
本文提出SafeDec框架，通过约束解码确保机器人导航基础模型满足信号时间逻辑规范，提升安全性和行动生成能力。
摘要：
近期，机器人基础模型的发展取得了进展，导致机器人系统在端到端和通用能力方面表现出色。这些策略在大量的模拟和现实轨迹数据集上进行训练，将多模态观测直接映射到动作序列以进行物理执行。尽管具有出色的现实能力，这些模型仍然数据驱动，因此缺乏行为正确性的显式概念。我们通过引入SafeDec，一种用于自回归、基于变压器的机器人导航基础模型的约束解码框架，来解决这一差距。该框架强制执行以信号时间逻辑（STL）公式表达的安全规范。我们的方法确保生成的动作在假设的动力学下在运行时能够证明满足STL规范，而无需重新训练，同时不关心底层策略。我们在CHORES基准的多个任务上评估了SafeDec，展示了我们的解码时干预不仅可用于过滤不安全的动作，还用于条件动作生成。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
