Setting up a Reinforcement Learning Task with a Real-World Robot (1803.07067v1)

Abstract: 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. We find that learning performance can be highly sensitive to the setup, and thus oversights and omissions in setup details can make effective learning, reproducibility, and fair comparison hard. Our study suggests some mitigating steps to help future experimenters avoid difficulties and pitfalls. We show that highly reliable and repeatable experiments can be performed in our setup, indicating the possibility of reinforcement learning research extensively based on real-world robots.

• The paper proposes a detailed framework for setting up reliable reinforcement learning tasks on a real-world robot, exemplified by a UR5 arm performing a Reacher task.
• Key findings highlight the importance of system setup choices, showing that Ethernet, velocity control, and optimized cycle times significantly improve real-world RL performance over Wi-Fi or position control.
• This research provides practical guidance and theoretical insights, paving the way for reproducible physical robot RL experiments beyond simulation and informing future algorithm designs.

Overview of Real-World Reinforcement Learning Task Setup with UR5

This paper addresses a critical challenge in reinforcement learning (RL) research—deploying RL agents to learn tasks using real-world robots. Historically, the difficulty and unreliability of learning with physical robots have limited RL's application beyond simulations. The paper explores the components and interactions necessary to configure a real-world RL task effectively, exemplified by a UR5 robotic arm, aiming to facilitate reliable and reproducible experimentation.

Task Setup and Methodology

The authors developed a learning environment using the UR5 robotic arm to perform the “Reacher” task, wherein the robot learns to manipulate its joints to achieve specific spatial targets. The setup mirrored the OpenAI Gym's Reacher environment to ensure familiarity and benchmark comparisons. The robotic task incorporates real-world complexities such as sensor noise and actuation delays, which are abstracted away in simulated environments. Reinforcement learning was implemented via the TRPO algorithm, known for its robustness across hyper-parameter choices, to systematically evaluate task setup variations.

Key aspects examined include:

• Data Transmission Medium: Network configuration impacts latency significantly. The paper showed that Ethernet connections provide consistent inter-arrival times for data packets compared to Wi-Fi, which is prone to higher variability and latency.
• Concurrency, Ordering, and Delays: Efficient asynchronous task setup minimizes latency by decoupling agent and environment computations, advocating concurrent rather than sequential execution found in simulations.
• Action Space Definition: Choice between position and velocity control interfaces significantly affects immediate motor responses. Velocity control presented clear advantages in direct manipulation effectiveness.
• Action Cycle Time: The paper underscored the importance of optimizing action cycle time—shorter times complicate learning, while excessively long cycles hinder learning rate and precision.

Results and Implications

The paper highlights the sensitivity of real-world RL performance to setup nuances, particularly in communication and control strategies. Repeated experiments demonstrated consistent learning curves within the baseline configuration, confirming both reliability and robustness of the proposed experimental design. Variations in system setups, such as cycle times and control spaces, showcased significant differences in learning efficiency.

These findings imply several critical insights:

• Practical Guidance: Specific recommendations such as using wired connections and optimizing cycle times provide practical benchmarks for designing real-world RL tasks.
• Theoretical Insights: The interplay between computational concurrency and real-time adaptability can inspire novel RL algorithmic designs tailored for physical environments.
• Research Implications: The framework for establishing task setups with real-world robots opens avenues for extensive RL work beyond simulations. It emphasizes the potential for reproducible real-world experimentation despite intrinsic environmental variability.

This work is foundational for the broader RL community, offering a structured pathway to bridge the gap between simulation-based benchmarks and real-world robotic implementations. As physical RL applications expand, future research may further explore adaptive algorithms capable of dynamic environmental interaction, paving the way for increasingly autonomous and intelligent robotic systems.