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Reinforcement learning is a promising approach to developing hard-to-engineeradaptive solutions for complex and diverse robotic tasks. However, learningwith real-world robots is often unreliable and difficult, which resulted intheir low adoption in reinforcement learning research. This difficulty isworsened 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 tolight some key elements of a task setup and study their contributions to thechallenges with robots. We find that learning performance can be highlysensitive to the setup, and thus oversights and omissions in setup details canmake effective learning, reproducibility, and fair comparison hard. Our studysuggests some mitigating steps to help future experimenters avoid difficultiesand pitfalls. We show that highly reliable and repeatable experiments can beperformed in our setup, indicating the possibility of reinforcement learningresearch extensively based on real-world robots.

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