🤖 AI Summary
In automated chemical experimentation, tight coordination between robotic manipulator motions and fixture operations remains challenging. Method: This paper proposes a dual-imitation teaching paradigm that synchronously demonstrates both the end-effector pose of a mobile manipulator and the state transitions of experimental fixtures, thereby unifying motion control and fixture logic modeling to enable end-to-end replication of liquid-phase operations (e.g., pipetting, dilution). The system requires no programming and enables non-expert users to rapidly deploy multi-step experimental protocols. Contribution/Results: The framework integrates a mobile manipulator, reconfigurable fixtures, motion-capture interfaces, and a coordinated control module. Evaluated on polymer synthesis tasks, it achieves high motion reproduction accuracy, task success rate >95%, and continuous multi-day autonomous operation. This approach significantly enhances robustness, usability, and scalability in automated chemical experimentation.
📝 Abstract
While robotic automation has demonstrated remarkable performance, such as executing hundreds of experiments continuously over several days, it is challenging to design a program that synchronizes the robot's movements with the experimental jigs to conduct an experiment. We propose a concept that enables the automation of experiments by utilizing dual demonstrations of robot motions and jig operations by chemists in an experimental environment constructed to be controlled by a robot. To verify this concept, we developed a chemical-experiment-automation system consisting of jigs to assist the robot in experiments, a motion-demonstration interface, a jig-control interface, and a mobile manipulator. We validate the concept through polymer-synthesis experiments, focusing on critical liquid-handling tasks such as pipetting and dilution. The experimental results indicate high reproducibility of the demonstrated motions and robust task-success rates. This comprehensive concept not only simplifies the robot programming process for chemists but also provides a flexible and efficient solution to accommodate a wide range of experimental conditions, contributing significantly to the field of chemical experiment automation.