Laboratory Automation

Laboratory automation aims to increase efficiency and reproducibility in scientific experimentation by automating tasks traditionally performed manually. Current research emphasizes developing flexible robotic systems capable of interacting with diverse lab equipment and adapting to various experimental procedures, often employing computer vision, machine learning (including reinforcement learning and probabilistic programming), and advanced planning algorithms (like PDDLStream) for task execution and control. This automation holds significant promise for accelerating scientific discovery across diverse fields, from chemistry and materials science to biology and medicine, by reducing human workload, improving data quality, and enabling high-throughput experimentation.