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This research establishes a formal computational interface between AI agent reasoning and physical laboratory operations, enabling verifiable, autonomous scientific discovery workflows. By representing the physical lab as executable programs with explicit constraints, the system allows agents to safely plan complex experiments involving iteration and concurrency before execution. This framework is a foundational step toward fully automated end-to-end scientific research where AI directly controls robotic hardware with guaranteed safety preconditions.
A computable representation of the physical laboratory enables verifiable workflows
arXiv:2609.03621v1 Announce Type: new Abstract: Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computable representation of the physical laboratory is established through typed research objects, capability-bound operations and a compositional workflow algebra. It provides the physical-world counterpart to machine-readable knowledge, expressing workflows as programs over evolving laboratory states with explicit dependencies, decisions, iteration and concurrency. The representation was implemented in a modular agentic robotic laboratory by binding formal operations to executable Function Skills. For diverse scientific intents, capability-relative workflows were generated, while stateful simulation propagated object transformations and verified operation preconditions and laboratory constraints before dispatch. The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.
Abstract:Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computable representation of the physical laboratory is established through typed research objects, capability-bound operations and a compositional workflow algebra. It provides the physical-world counterpart to machine-readable knowledge, expressing workflows as programs over evolving laboratory states with explicit dependencies, decisions, iteration and concurrency. The representation was implemented in a modular agentic robotic laboratory by binding formal operations to executable Function Skills. For diverse scientific intents, capability-relative workflows were generated, while stateful simulation propagated object transformations and verified operation preconditions and laboratory constraints before dispatch. The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.
Artificial Intelligence (cs.AI)
arXiv:2609.03621 [cs.AI]
(or arXiv:2609.03621v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.03621 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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