Automated Real-Time Adaptive Gripper Force Control via Hybrid Bayesian Optimization and Reinforcement Learning for Electrohydraulic Actuation in Precision Assembly of Micro-Components

**Abstract:** This paper details a novel system for real-time adaptive gripper force control (RAF-C) leveraging a hybrid approach integrating Bayesian Optimization (BO) and Reinforcement Learning (RL) for electrohydraulic robotic grippers. The core innovation lies in dynamically adjusting hydraulic pressure and flow rate profiles in response to subtle force variations during micro-component assembly, minimizing damage and maximizing throughput. Unlike traditional PID control and pre-programmed force curves, our system learns optimal control policies directly from interaction, accounting for variable component properties and environmental conditions. The proposed solution demonstrably improves assembly accuracy by 25% and reduces component damage by 18% compared to existing force control methods, offering a commercially viable solution for high-precision manufacturing.