Overview

Robotic laboratory automation has been explored to alleviate the researchers' efforts to manually operate many complex and time-consuming laboratory procedures. Moreover, robots can reduce human errors, enhance reproducibility, and handle hazardous materials. Thus, this study focuses on a robotic powder weighing task used in laboratory automation. In this task, a robot weighs a certain amount of powder with a milligram-level target mass using a dispensing spoon. The complex dynamics of the powder, the variations in the materials being weighed, and the need to balance conservative and aggressive actions are significant challenges in the robotics field. Therefore, learning approaches are critical for this task. However, many learning interactions in real-world environments require substantial efforts to clean the spread powder. To overcome this issue, this study employs a sim-to-real transfer learning approach using a domain randomization (DR) technique. This enables the robot to weigh various powders with a small target mass and alleviates the burden of collecting data in a real-world environment. Herein, we formulated weighing manipulation as a reinforcement learning problem. Besides, we developed a powder weighing simulator and carefully selected the dynamics parameters used for DR to adapt to unseen environments. A recurrent neural network-based policy was adopted considering the balance of conservative and aggressive actions. The sim-to-real zero-shot transfer experiments demonstrated that the robot completed the weighing tasks with an average weighing error of 0.1 -- 0.2 mg for different powder materials and target masses (5 -- 15 mg). Overall, this approach shows promising results and can be useful for automating laboratory tasks that involve weighing powders.

Video

Contact

Citation

@inproceedings{kadokawa2023learning,
  title={Learning Robotic Powder Weighing from Simulation for Laboratory Automation},
  author={Kadokawa, Yuki and Hamaya, Masashi, and Tanaka, Kazutoshi},
  booktitle={The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}