让机器人听懂安全指令，实时执行复杂语义约束
From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation
- 用大模型将自然语言转为结构化安全规范
- 结合3D环境感知与实时控制，精准执行约束
- 支持多种场景，适合人机协作的开放环境
随着机器人越来越多地融入开放世界、以人类为中心的环境，其理解自然语言指令并遵守安全约束的能力对实现有效且可信的交互至关重要。现有方法通常将语言映射到奖励函数而非安全规范，或仅处理狭窄的约束类别（如避障），限制了其鲁棒性和适用性。本文提出一种模块化语言条件安全框架用于机器人导航。该框架包含三个核心组件：(1) 基于大语言模型（LLM）的模块，将自由形式的指令转化为结构化安全规范；(2) 感知模块，通过维护环境中物体级的3D表示来具象化这些规范；(3) 基于模型预测控制（MPC）的安全过滤器，实时强制执行语义与几何约束。通过仿真与硬件实验评估，证明该框架在多种环境与场景中能稳健解析并执行多样化的语言指定约束。
原文摘要 · Abstract (English)
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.
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