ACT Action Chunk Adaptive Optimization Method for Chemistry-oriented Robots

Authors

• Tan Li, School of Information Engineering, Nanchang University, Nanchang 330000, China; Quiktech (Beijing) Technology Co., Ltd., Beijing 100080, China
• Congying Wu, School of Information Engineering, Nanchang University, Nanchang 330000, China
• Shenghua Li, School of Information Engineering, Nanchang University, Nanchang 330000, China
• Nanjiang Chen, School of Information Engineering, Nanchang University, Nanchang 330000, China; Quiktech (Beijing) Technology Co., Ltd., Beijing 100080, China
• Weining Song, School of Information Engineering, East China University of Technology, Nanchang 330000, China; Nanchang Key Laboratory of Embodied Metaverse Key Technology R&D and Application, Nanchang 330000, China
• Fangfang Gao, School of Information Engineering, Nanchang University, Nanchang 330000, China; Nanchang Key Laboratory of Embodied Metaverse Key Technology R&D and Application, Nanchang 330000, China
• Qi Wang, Quiktech (Beijing) Technology Co., Ltd., Beijing 100080, China; Nanchang Key Laboratory of Embodied Metaverse Key Technology R&D and Application, Nanchang 330000, China
• Yanwen Lin, Quiktech (Beijing) Technology Co., Ltd., Beijing 100080, China; Nanchang Key Laboratory of Embodied Metaverse Key Technology R&D and Application, Nanchang 330000, China
• Runmin Yin, Quiktech (Beijing) Technology Co., Ltd., Beijing 100080, China; Nanchang Key Laboratory of Embodied Metaverse Key Technology R&D and Application, Nanchang 330000, China

Abstract

Abstract: Chemical R&D laboratories face persistent challenges such as long R&D cycles and high experimental repetitiveness. With the growing demand for automation and intelligence in laboratory processes, embodied intelligent robots capable of learning expert operational experience and executing tasks stably in complex environments have become a key direction for improving chemical R&D efficiency and reducing human error. Although the hardware and software for embodied intelligence are advancing rapidly, current research focuses primarily on general-purpose operations, with limited attention to the specific constraints of chemistry laboratory settings. Traditional ACT algorithms, which use fixed action chunk sizes, fail to meet the requirements of chemical operations. Aims to optimize imitation learning algorithms for chemical scenarios by proposing a task-aware adaptive action chunk size optimization framework based on ACT. On the hardware side, the system uses a Unitree G1-29edu robot equipped with an inspire RH56DFXW dexterous hand, along with a UR5 6-degree-of-freedom industrial manipulator fitted with a flexible gripper. A teleoperation system is used to collect a dataset of chemical operation demonstrations. A quantitative indicator system for task features is then established based on three dimensions: motion smoothness, precision requirements, and task duration, enabling adaptive adjustment of action chunk sizes. Additionally, to address the sensitivity of chemical operations to jitter, a motion smoothing module is added at the model output. Experimental results show that compared with the original ACT algorithm, the adaptive action chunk size strategy achieves significant improvements in task success rate, motion smoothness, and task duration. For most tasks, the completion time is reduced to below 85% of that of the original algorithm, and in some tasks, to below 66.7%. Motion smoothness improves by 12% to 25%, and task success rates in chemistry laboratory scenarios increase by 10% to 20%. For tasks requiring high precision and smoothness, the success rate improvement exceeds 25%.

Recommended Citation

Li, Tan; Wu, Congying; Li, Shenghua; Chen, Nanjiang; Song, Weining; Gao, Fangfang; Wang, Qi; Lin, Yanwen; and Yin, Runmin (2026) "ACT Action Chunk Adaptive Optimization Method for Chemistry-oriented Robots," Journal of System Simulation: Vol. 38: Iss. 9, Article 8.
DOI: 10.16182/j.issn1004731x.joss.26-0061
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss9/8

First Page

2559

Last Page

2573

CLC

TP242.6

Recommended Citation

Li Tan, Wu Congying, Li Shenghua, et al. ACT Action Chunk Adaptive Optimization Method for Chemistry-oriented Robots[J]. Journal of System Simulation, 2026, 38(9): 2559-2573.

Corresponding Author

Chen Nanjiang

DOI

10.16182/j.issn1004731x.joss.26-0061