Robotic integration for end-stations at scientific user facilities

Abstract

The integration of robotics and artificial intelligence (AI) into scientific workflows is transforming experimental research, particularly at large-scale user facilities such as the National Synchrotron Light Source II (NSLS-II). We present an extensible architecture for robotic sample management that combines the Robot Operating System 2 (ROS2) with the Bluesky experiment orchestration ecosystem. This approach enabled seamless integration of robotic systems into high-throughput experiments and adaptive workflows. Key innovations included a client-server model for managing robotic actions, real-time pose estimation using fiducial markers and computer vision, and closed-loop adaptive experimentation with agent-driven decision-making. Deployed using widely available hardware and open-source software, this architecture successfully automated a full shift (8 hours) of sample manipulation without errors. The system's flexibility and extensibility allow rapid re-deployment across different experimental environments, enabling scalable self-driving experiments for end stations at scientific user facilities. This work highlights the potential of robotics to enhance experimental throughput and reproducibility, providing a roadmap for future developments in automated scientific discovery where flexibility, extensibility, and adaptability are core requirements.