The ADePT framework for assessing autonomous laboratory robotics

TL;DR

This paper introduces the ADePT framework to evaluate and guide the development of autonomous laboratory robotics.

Contribution

The ADePT framework provides a structured benchmark for assessing robotic capabilities in self-driving laboratories.

Findings

The ADePT framework identifies four core dimensions: adaptability and learning, dexterity, perception, and task complexity.

The framework helps expose bottlenecks and guide the design of autonomous laboratory ecosystems.

Future directions include robot-centric integration and collaborative human–robot environments.

Abstract

Laboratory robotics is advancing from routine automation toward autonomous systems capable of intelligent decision-making and flexible execution. This perspective outlines key milestones and introduces the ADePT framework, which defines four core dimensions of robotic capability proficiency: adaptability and learning, dexterity, perception, and task complexity. We discuss future directions for self-driving laboratories, including robot-centric, end-to-end robotic integration, and collaborative human–robot environments. These scenarios highlight the importance of technological enablers and evolving regulatory paradigms. By connecting present technologies to emerging system configurations, this work offers a foundation for designing autonomous laboratory ecosystems that support scientific discovery and operational efficiency.

Laboratory robotics is shifting from scripted automation towards autonomous systems that can perceive, decide and act robustly in real experimental environments. Here, the authors introduce the ADePT framework, comprising adaptability and learning, dexterity, perception and task complexity, to benchmark robotic capability, expose key bottlenecks and chart practical routes towards truly self-driving laboratories.

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