构建可更新的显式世界模型，让机器人更懂人类意图。
Explicit World Models for Reliable Human-Robot Collaboration
- 用显式世界模型捕捉人机交互中的共同认知
- 在感知噪声和指令模糊时仍保持行为一致
- 适合需要高可靠性的协作机器人场景
本文针对感知噪声、指令模糊及人机交互中的可靠性问题，提出一种全新思路：不依赖形式化验证保证模型可预测性，而是强调人机交互的动态性、模糊性和主观性。当具身智能体运行于社交、多模态且流动的人类环境时，可靠性取决于人类目标与期望，需通过构建并持续更新一个可访问的‘显式世界模型’来实现。该模型代表人与人工智能之间的共同认知基础，用于对齐机器人行为与人类预期，从而提升交互的可理解性与一致性。
原文摘要 · Abstract (English)
This paper addresses the topic of robustness under sensing noise, ambiguous instructions, and human-robot interaction. We take a radically different tack to the issue of reliable embodied AI: instead of focusing on formal verification methods aimed at achieving model predictability and robustness, we emphasise the dynamic, ambiguous and subjective nature of human-robot interactions that requires embodied AI systems to perceive, interpret, and respond to human intentions in a manner that is consistent, comprehensible and aligned with human expectations. We argue that when embodied agents operate in human environments that are inherently social, multimodal, and fluid, reliability is contextually determined and only has meaning in relation to the goals and expectations of humans involved in the interaction. This calls for a fundamentally different approach to achieving reliable embodied AI that is centred on building and updating an accessible "explicit world model" representing the common ground between human and AI, that is used to align robot behaviours with human expectations.
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