Towards cost-effective and safe contact-rich robotic manipulation with reinforcement learning: A review of techniques for future industrial automation

Reinforcement Learning (RL) has been considered a promising method to enable the automation of contact-rich manipulation tasks, which can increase capabilities for industrial automation. RL facilitates autonomous agents’ learning to solve environments with complex dynamics with little human intervention, making it easier to implement control strategies for contact-rich tasks compared to traditional control approaches. Further, RL-based robotic control has the potential to transfer policies between task variations, significantly improving scalability compared to existing methods. However, RL is currently inviable for wider adoption due to its relatively high implementation costs and safety issues, so current research has been focused on addressing these issues. This paper comprehensively reviewed recently developed techniques to improve cost and safety for RL in contact-rich robotic manipulation. Techniques were organized by their approach, and their impact was analysed. It was found that current research efforts have significantly improved the cost and safety of RL-based control for contact-rich tasks, but further improvements can be made by progressing research towards improving knowledge transfer between tasks, improving inter-robot policy transfer and facilitating real-world and continual RL. The identified directions for further research set the stage for future developments in more versatile and cost-effective RL-based control for contact-rich robotic manipulation in future industrial automation applications.