从词语到安全：面向机器人导航的语言条件安全过滤
From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation
AI总结：
本文提出一个模块化框架，将自然语言指令转换为结构化安全规范，并结合物体级三维感知与模型预测控制安全过滤器，在机器人导航中实时执行语义和几何约束。
摘要：
随着机器人日益融入开放世界、以人为中心的环境，其解释自然语言指令并遵守安全约束的能力对于有效且可信的交互至关重要。现有方法通常侧重于将语言映射为奖励函数而非安全规范，或仅处理狭窄的约束类别（例如避障），这限制了它们的鲁棒性和适用性。我们提出了一个用于机器人导航中语言条件安全的模块化框架。该框架由三个核心组件组成：（1）一个基于大语言模型（LLM）的模块，将自由形式的指令转换为结构化安全规范；（2）一个感知模块，通过维护环境的物体级三维表示来落实这些规范；（3）一个基于模型预测控制（MPC）的安全过滤器，实时执行语义约束和几何约束。我们通过仿真研究和硬件实验评估了所提框架的有效性，证明其能够在广泛的环境和场景中鲁棒地解释并执行多样化的语言指定约束。
英文摘要：
As robots become increasingly integrated into open-world, human-centered environments, their ability to interpret natural language instructions and adhere to safety constraints is critical for effective and trustworthy interaction. Existing approaches often focus on mapping language to reward functions instead of safety specifications or address only narrow constraint classes (e.g., obstacle avoidance), limiting their robustness and applicability. We propose a modular framework for language-conditioned safety in robot navigation. Our framework is composed of three core components: (1) a large language model (LLM)-based module that translates free-form instructions into structured safety specifications, (2) a perception module that grounds these specifications by maintaining object-level 3D representations of the environment, and (3) a model predictive control (MPC)-based safety filter that enforces both semantic and geometric constraints in real time. We evaluate the effectiveness of the proposed framework through both simulation studies and hardware experiments, demonstrating that it robustly interprets and enforces diverse language-specified constraints across a wide range of environments and scenarios.
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。
