UniROS: ROS-Based Reinforcement Learning Across Simulated and Real-World Robotics

Source abstract

Reinforcement Learning (RL) enables robots to learn and improve from data without being explicitly programmed. It is well-suited for tackling complex and diverse robotic tasks, offering adaptive solutions without relying on traditional, hand-designed approaches. However, RL solutions in robotics have often been confined to simulations, with challenges in transferring the learned knowledge or learning directly in the real world due to latency issues, lack of standardized structure, and the complexity of integrating with real robot platforms. Furthermore, existing robotic RL frameworks typically support sequential, turn-based agent-environment interactions, which fail to represent the continuous, dynamic nature of real-time robotics. This paper addresses this gap by proposing UniROS, a novel Robot Operating System (ROS)-based RL framework explicitly designed for real-time multi-robot/task applications. UniROS introduces a ROS-centric implementation strategy for creating RL environments that support asynchronous, concurrent processing, which is pivotal in reducing the latency between agent-environment interactions. This study validates UniROS through practical robotic scenarios, including direct real-world learning, sim-to-real policy transfer, and concurrent multi-robot/task learning. The proposed framework, including all the examples and supporting packages developed in this study, is publicly available on GitHub, inviting wider use and exploration in the field.