Intelligent Robotic Manipulation for IV Bag Quality Control: A Contamination-Aware Learning System

Abstract

This paper presents a novel contamination-aware robotic manipulation framework for real-time IV bag quality assessment with adaptive manipulation policies. The system employs dual S0-ARM101 low-cost robot arms with eye-in-hand RGB vision for IV bag handling, coordinated through an open-source ROS2 architecture. Using a leader-follower architecture, the system learns visual-motor mappings from just 50 expert demonstrations, achieving 96% and 98% success rates for loading and unloading operations, respectively. The contamination-aware approach demonstrates 94% success to detected contaminated IV bags, and 90% adaptation to new scenarios, with zero safety incidents during testing. Statistical validation with 95% confidence intervals confirms the system’s reliability, while the integration of quality feedback into manipulation decision-making creates a responsive system that adapts to contamination detection results in real-time. This work demonstrates that learned visual-motor mappings with contamination awareness can significantly enhance pharmaceutical quality control while maintaining safety, establishing an economically viable framework for intelligent manufacturing automation.