Advanced robot systems are enablers for achieving greater flexibility and adaptability, however, programming such systems also become increasingly complex. Consequently, new methods for programming robot systems and enabling self-learning capabilities to accommodate the natural variation exhibited in real-world tasks are needed.
In this work we present a Reinforcement Learning enabled robot system, which learns task trajectories from human workers. The work demonstrates that with minimal human effort, we can transfer manual manipulation tasks in certain domains to a robot system without the requirement for a complicated hardware system model or tedious and complex programming. The robot is able to build upon the learned concepts from the human expert and improve its performance over time. Applying Reinforcement Learning for industrial robotics and processes, holds and unseen potential, especially for tasks where natural variation is exhibited in either the product or process.
