Developing dRTP Leadership for Reliable AI-Driven Autonomous Laboratories

This pilot project will focus on developing people and professional practice around autonomous laboratories. AI systems and robots are increasingly being used to plan, control, and automate laboratory experiments across synthetic biology, chemistry, materials science, physics, and other data-intensive fields. These autonomous systems promise faster discovery, but they also create new responsibilities for digital Research Technical Professionals (dRTPs), who must learn to supervise, troubleshoot, and improve them safely.

At present, there is limited shared guidance to help technical professionals develop the leadership skills, confidence, and cross-disciplinary language needed to manage AI-driven experimentation. The project will help dRTPs understand common failure modes in AI-agent-driven robotic experiments, make better decisions about oversight and recovery, and communicate more effectively with experimental researchers and AI/robotics specialists.

To support this development, the project will produce reusable open resources: a failure taxonomy with evidence-informed responses, an application-agnostic reliability test guide, and a lightweight stress-test framework.

The project will benefit dRTPs and researchers working with autonomous experimentation by strengthening their ability to deploy, supervise, and improve these systems confidently. It will also support Dr Xintong Yang’s development as a community leader bridging laboratory science with AI and robotics, creating a foundation for future training, community development, and larger-scale funding.