让AI像科学家一样动手实验，自动完成从假设到验证的全流程。
Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
- 构建感知-语言-行动-发现一体化框架，实现智能体自主实验。
- 通过物理反馈闭环，将计算预测与真实实验结果紧密连接。
- 适合希望实现自动化科研的实验室或生物医药研究者。
人工智能在预测科学属性方面已展现强大能力，但科学发现本质上是受实验周期驱动的长期、物理性探索过程。当前多数计算方法与这一现实脱节，将发现视为孤立的任务式预测，而非与物理世界的持续互动。本文提出‘具身科学’范式，将科学发现重构为一个闭环系统，紧密耦合代理式推理与物理执行。我们提出统一的感知-语言-行动-发现（PLAD）框架：具身智能体可感知实验环境，基于科学知识进行推理，执行物理干预，并将结果内化以指导后续探索。通过将计算推理扎根于可靠的物理反馈，该方法弥合了数字预测与实证验证之间的鸿沟，为生命科学和化学领域中的自主发现系统提供了可行路径。
原文摘要 · Abstract (English)
Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed by experimental cycles. Most current computational approaches are misaligned with this reality, framing discovery as isolated, task-specific predictions rather than continuous interaction with the physical world. Here, we argue for embodied science, a paradigm that reframes scientific discovery as a closed loop tightly coupling agentic reasoning with physical execution. We propose a unified Perception-Language-Action-Discovery (PLAD) framework, wherein embodied agents perceive experimental environments, reason over scientific knowledge, execute physical interventions, and internalize outcomes to drive subsequent exploration. By grounding computational reasoning in robust physical feedback, this approach bridges the gap between digital prediction and empirical validation, offering a roadmap for autonomous discovery systems in the life and chemical sciences.
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