Curiosity Based Reinforcement Learning on Robot Manufacturing Cell

TL;DR

This paper demonstrates that curiosity-based reinforcement learning enables flexible, transferable control in robot manufacturing cells, reducing the need for manual reward tuning and improving adaptability across different environments.

Contribution

It introduces a curiosity-driven RL approach for scheduling in robot manufacturing, showing successful transferability between different cell structures.

Findings

Agents solved both structured and graph-structured environments.

Curiosity modules transferred successfully between environments.

Curiosity-based RL outperforms traditional reward shaping methods.

Abstract

This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem. Further, the learning agents are embedded into the transportation robots to enable a generalized learning solution that can be applied to a variety of environments. In the first approach, the curiosity based reinforcement learning is applied to a simple structured robot manufacturing cell. And in the second approach, the same algorithm is applied to a graph structured robot manufacturing cell. Results from the experiments show that the agents are able to solve both the environments with the ability to transfer the curiosity module directly from one environment to another. We conclude that curiosity based learning on scheduling tasks provide a viable alternative to the reward shaped reinforcement learning traditionally used.