AgentChemist: A Multi-Agent Experimental Robotic Platform Integrating Chemical Perception and Precise Control

TL;DR

AgentChemist is a multi-agent robotic platform that combines chemical perception and adaptive control to perform diverse laboratory tasks with flexibility and real-time feedback, surpassing traditional rigid automation systems.

Contribution

It introduces a novel multi-agent system that integrates chemical perception and dynamic scheduling to handle long-tail, diverse laboratory experiments.

Findings

Successful autonomous acid-base titration with real-time monitoring

Adaptive dispensing control based on feedback

Enhanced generalization to diverse laboratory scenarios

Abstract

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.