William Hebberd

Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

This study investigates the use of adapters in reinforcement learning for robotic skill generalization across multiple robots and tasks. Traditional methods are typically reliant on robot-specific retraining and face challenges such as efficiency and adaptability, particularly when scaling to robots