Setting up a Reinforcement Learning Task with a Real-World Robot

Understand

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks.

• However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research.
• This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots.
• In this work, we develop a learning task with a UR5 robotic arm to bring to light some key elements of a task setup and study their contributions to the challenges with robots.