RoboChemGym creates simulated lab tasks for chemical robots
RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation
Robotics
Summary
Lab experiments can be slow, expensive, and risky. The authors created RoboChemGym, a tool that simulates detailed chemical experiments so robots can practice them. This system generates step-by-step expert demonstrations, improving itself over time to handle complex chemical tasks with many steps. It also tests robot performance from simple actions to full experiments, helping automate lab work.
What this means in practice
- •For robotics engineers: Generate expert demonstrations of complex chemical procedures to train and evaluate lab automation robots safely and cost-effectively.
- •For chemical process designers: Test and refine multi-step chemical protocols in simulation before physical implementation to reduce risk and improve procedure design.
Authors
Chenxi Li, Haiyuan Wan, Rui Li, Jingyuan Li, Sha Zhang, Bohan Feng, Jianbao Cao, Zhangrui Zhao, Di Hu, Wangmeng Zuo, Shixiang Tang, Minting Pan, Dongzhan Zhou
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.