Approach for Task Transfer and Demonstration Learning Optimization in Industrial Robotic Manipulators

This paper proposes a refined method to enhance the performance and adaptability of industrial robotic manipulators by integrating demonstration learning with reinforcement learning, specifically using Proximal Policy Optimization (PPO). The approach starts with generating an initial trajectory through demonstration-based learning, which provides a robust foundation. Subsequently, PPO is employed to fine-tune the policy, improving precision while minimizing the amount of data required. Experimental evaluations reveal that our method achieves a 15% reduction in training data needs compared to standard approaches, along with enhanced adaptability across different tasks. This makes the method well-suited for flexible manufacturing settings where rapid task transfer is crucial.