Abstract
Wet-lab experimentation serves as the gold standard for hypothesis verification in scientific discovery; yet it is inherently labor-intensive, costly, and safety-critical. Embodied agents hold the promise of automating these tedious workflows, but their development is hindered by the scarcity of real-world training data. While simulation offers a scalable alternative for producing demonstrations, current methods primarily target relatively short-horizon tasks with loosely structured interactions, failing to meet the strict procedural constraints and fine-grained manipulation demands of chemical experiments. To bridge this gap, we introduce \textbf{RoboChemGym}, a framework that autonomously generates high-fidelity manipulation demonstrations aligned with real-world experiment protocols, featuring a \textit{self-improving task synthesis} mechanism to iteratively refine task execution and scene configurations, enabling the reliable generation of expert trajectories for complex, multi-object protocols exceeding 10 interaction steps. Furthermore, we introduce a hierarchical benchmark that systematically assesses performance across varying granularities, spanning from atomic operations to full-cycle experimental workflows. RoboChemGym sets a scalable paradigm for the automated data synthesis and capability evaluation of embodied agents in intricate chemical tasks, serving as a critical stepping stone toward fully intelligent laboratories.
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- Green open access
Cite this article
APA 7
Li, C., Wan, H., Li, R., Li, J., Zhang, S., Feng, B., Cao, J., Zhao, Z., Hu, D., Zuo, W., Tang, S., Pan, M., & Zhou, D. (2026). RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation. https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation
MLA 9
Li, Chenxi, et al. "RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation." https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation.
Chicago (author–date)
Li, Chenxi, Haiyuan Wan, Rui Li, Jingyuan Li, Sha Zhang, Bohan Feng, Jianbao Cao, Zhangrui Zhao, Di Hu, Wangmeng Zuo, Shixiang Tang, Minting Pan, and Dongzhan Zhou. 2026. "RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation." https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation.
Harvard
Li, C., Wan, H., Li, R., Li, J., Zhang, S., Feng, B., Cao, J., Zhao, Z., Hu, D., Zuo, W., Tang, S., Pan, M. and Zhou, D. (2026) 'RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation', Available at: https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation.
Vancouver
Li C, Wan H, Li R, Li J, Zhang S, Feng B, et al. RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation. https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation
IEEE
C. Li, H. Wan, R. Li, J. Li, S. Zhang, B. Feng, J. Cao, Z. Zhao, D. Hu, W. Zuo, S. Tang, M. Pan, and D. Zhou, "RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation," https://omanscience.com/en/articles/robochemgym-a-protocol-driven-generative-simulation-framework-for-long-horizon-chemical-manipulation.