Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
This addresses the problem of automating scientific discovery for researchers in fields like life and chemical sciences, though it is a new paradigm rather than an incremental improvement.
The paper tackles the misalignment between computational prediction and the physical, iterative nature of scientific discovery by proposing embodied science, a paradigm that integrates agentic reasoning with physical execution through a PLAD framework to enable autonomous discovery systems in life and chemical sciences.
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.