用可信赖的规则与自我怀疑机制，让机器人在复杂环境中安全合规运行。
The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents
- 融合符号逻辑与深度学习，构建可解释的决策框架
- 通过速度、传感器等特征动态生成怀疑概率，提升适应性
- 适合自动驾驶、无人机等高安全要求场景
在不确定环境中的自主代理行为可靠性与规则合规性仍是现代机器人学的核心挑战。本文提出神经符号系统，将概率性、符号化的白盒推理模型与深度学习方法结合，同时处理显式规则与噪声数据训练的神经模型，实现结构化推理与灵活表征的协同。为此，我们引入宪法控制器（CoCo），一种基于深度概率逻辑程序推理约束的新型框架，适用于共享交通空间等场景。进一步提出自怀疑概念，以旅行速度、传感器状态或健康状况等特征为条件，构建怀疑概率密度函数。在真实世界空中出行研究中，验证了CoCo能帮助智能自主系统学习合理怀疑，在复杂不确定环境中安全合规导航。
原文摘要 · Abstract (English)
Ensuring reliable and rule-compliant behavior of autonomous agents in uncertain environments remains a fundamental challenge in modern robotics. Our work shows how neuro-symbolic systems, which integrate probabilistic, symbolic white-box reasoning models with deep learning methods, offer a powerful solution to this challenge. This enables the simultaneous consideration of explicit rules and neural models trained on noisy data, combining the strength of structured reasoning with flexible representations. To this end, we introduce the Constitutional Controller (CoCo), a novel framework designed to enhance the safety and reliability of agents by reasoning over deep probabilistic logic programs representing constraints such as those found in shared traffic spaces. Furthermore, we propose the concept of self-doubt, implemented as a probability density conditioned on doubt features such as travel velocity, employed sensors, or health factors. In a real-world aerial mobility study, we demonstrate CoCo's advantages for intelligent autonomous systems to learn appropriate doubts and navigate complex and uncertain environments safely and compliantly.
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