[2603.19782] Embodied Science: Closing the Discovery Loop with Agentic Embodied AI

Computer Science > Artificial Intelligence arXiv:2603.19782 (cs) [Submitted on 20 Mar 2026] Title:Embodied Science: Closing the Discovery Loop with Agentic Embodied AI Authors:Xiang Zhuang, Chenyi Zhou, Kehua Feng, Zhihui Zhu, Yunfan Gao, Yijie Zhong, Yichi Zhang, Junjie Huang, Keyan Ding, Lei Bai, Haofen Wang, Qiang Zhang, Huajun Chen View a PDF of the paper titled Embodied Science: Closing the Discovery Loop with Agentic Embodied AI, by Xiang Zhuang and 12 other authors View PDF HTML (experimental) Abstract: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 ro...