Embodied Science: Closing the Discovery Loop with Agentic Embodied AI

TL;DR

This paper introduces embodied science, a new paradigm that integrates agentic reasoning with physical experimentation to enhance autonomous scientific discovery in life and chemical sciences.

Contribution

It proposes the PLAD framework, unifying perception, reasoning, action, and discovery for embodied agents to conduct continuous physical experiments.

Findings

Bridges the gap between digital prediction and empirical validation.

Provides a roadmap for autonomous discovery systems.

Emphasizes the importance of physical feedback in scientific reasoning.

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 roadmap for autonomous discovery systems in the life and chemical sciences.