AgentChemist: A Multi-Agent Experimental Robotic Platform Integrating Chemical Perception and Precise Control
Authors: Xiangyi Wei, Fei Wang, Haotian Zhang, Xin An, Haitian Zhu, Lianrui Hu, Yang Li, Changbo Wang, +1 more
Organizations: School of Computer Science and Technology, East China Normal University, Shanghai, China. · School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China. · School of Data Science and Engineering, East China Normal University, Shanghai, China. · Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, Shanghai, China.
Chemical laboratory automation has long been constrained by rigid workflows and poor adaptability to the long-tail distribution of experimental tasks. While most automated platforms perform well on a narrow set of standardized procedures, real laboratories involve diverse, infrequent, and evolving operations that fall outside predefined protocols. This mismatch prevents existing systems from generalizing to novel reaction conditions, uncommon instrument configurations, and unexpected procedural variations. We present a multi-agent robotic platform designed to address this long-tail challenge through collaborative task decomposition, dynamic scheduling, and adaptive control. The system integrates chemical perception for real-time reaction monitoring with feedback-driven execution, enabling it to adjust actions based on evolving experimental states rather than fixed scripts. Validation via acid-base titration demonstrates autonomous progress tracking, adaptive dispensing control, and reliable end-to-end experiment execution. By improving generalization across diverse laboratory scenarios, this platform provides a practical pathway toward intelligent, flexible, and scalable laboratory automation.
Figures & tables
Figure 1: The motivation for a multi-agent experimental robotic platform. Traditional rigid automation (left) is characterized by fixed protocols, blind execution, and fragile adaptation. In contrast, AgentChemist introduces dynamic workflows through multi-agent task decomposition, adaptive perception with real-time monitoring, and robust, instrument-integrated resilience to address long-tail challenges in the laboratory.
Figure 2: AgentChemist Workflow: AgentChemist receives user instructions, plans experimental protocols, drives robots to perform specific experimental operations, records results in real time, and finally returns the experimental report to the user.
Audio-based estimates Q^audio with confidence γa , statistical estimates Q^stat with confidence γs , fused high-confidence values Q^fused
D , high-confidence recorder
Table 1: Structured Knowledge Extracted for Experimental Report Generation
Figure 4: State Machine of the Statistics-based Chemical Quantity Logger
Figure 5: The Process of AgentChemist Driving the Evolution of FSM S in Acid-Base Titration Experiments
Figure 6: Acid-Base Titration Experiments. a Titration curve of maleic acid (concentration: 0.1M , initial volume: 25mL , initial pH=1.51 ). b-c Enlarged view of the transition region in the maleic acid titration curve. d-e First-derivative and second-derivative plots of the maleic acid titration curve. f Key conclusions from the Agent data analysis report for the maleic acid titration experiment. g-h Titration curves of hydrochloric acid and acetic acid (both at 0.1M concentration, initial volume: 25mL ). i Standard deviation plot comparing the hydrochloric acid titration curve with theoretical values (comparison of three parallel experiments and theoretical simulation).
Figure 7: Chemosensor with Color Perception. The color transition sequence (from left to right) during the complexometric titration of Ca2+ concentration using a 0.02M EDTA standard solution against a 0.01MCaCl2 solution (initial volume 5mL ) with a calcium indicator was as follows: the solution initially exhibits a magenta color due to the calcium–indicator complex, and finally turns sapphire at the endpoint upon the release of the free indicator.
No.
Weighing Error (g)
Time (s)
Success
1
0.08
45.2
Yes
2
0.11
47.8
Yes
3
0.05
43.5
Yes
4
0.15
52.1
Yes
5
0.09
46.3
Yes
6
0.07
44.9
Yes
Table 2: Experimental Results for Solid Weighing and Dissolution Task
Environmental Conditions
Success Rate
Average Weighing Error (g)
Standard Layout
98%
0.08
Disturbed Layout (Multiple Sizes of Beakers)
94%
0.11
Table 3: Generalization Performance of the Solution Preparation Task in Different Environments
Configuration
Task Completion Rate
pH Deviation
Anomaly Detection Accuracy
Control Group
96%
0.11
94%
Experiment Group A
92%
0.15
89%
Experiment Group B
78%
0.26
73%
Table 4: System Generalization Performance under Different Model Configurations
Figure 8: Configuration of the AgentChemist robotic experimental platform. The system features a Tracer 2.0 mobile base with all-terrain design and independent suspension for precise navigation. Two 7-DoF robotic arms (Piper Standard) are mounted on a rigid frame, offering 1.5 kg payload capacity and ±0.1 mm repeatability for precise laboratory manipulation. The perception system includes three Orbbec Dabai DC1 depth cameras (1920×1080, 30 fps) mounted on the wrists and front, plus two contact microphones embedded in the grippers to capture acoustic signatures during experimental operations.
Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA · Department of Chemistry, University of North Carolina at Chapel Hill, NC 27599, USA