SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
(2401.16013)Abstract
In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real world, and incorporate auxiliary data, such as demonstrations and prior experience. However, despite these advances, robotic RL remains hard to use. It is acknowledged among practitioners that the particular implementation details of these algorithms are often just as important (if not more so) for performance as the choice of algorithm. We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods. To address this challenge, we developed a carefully implemented library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment, a high-quality controller for a widely-adopted robot, and a number of challenging example tasks. We provide this library as a resource for the community, describe its design choices, and present experimental results. Perhaps surprisingly, we find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation between 25 to 50 minutes of training per policy on average, improving over state-of-the-art results reported for similar tasks in the literature. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent recovery and correction behaviors. We hope that these promising results and our high-quality open-source implementation will provide a tool for the robotics community to facilitate further developments in robotic RL. Our code, documentation, and videos can be found at https://serl-robot.github.io/
Overview
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A software framework named SERL aids in the practical deployment of RL for robots by emphasizing sample efficiency and incorporation of auxiliary data.
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SERL includes an off-policy deep RL method suited for tasks with image-based inputs and achieves a high update-to-data ratio.
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The algorithm integrates efficient learning techniques, such as symmetric sampling and layer-norm regularization, and automates reward function setup and environment resets.
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Validation shows SERL performing well in tasks like board insertion and cable routing with training times averaging 25 to 50 minutes per policy.
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SERL contributes to bridging the gap between theoretical RL advancements and real-world application, offering its software open-source.
Introduction
The landscape of robotic reinforcement learning (RL) has undergone substantial advancements, maturing to the point where robots can now engage in tasks from playing table tennis to routing cables - tasks that require fine manipulation skills and precise interaction with the environment. Despite recent algorithmic progress, practical deployment of robotic RL remains a significant challenge. Key to this challenge is the realization that specifics of algorithm implementations are often as pivotal to performance as the choice of the algorithm itself.
Software Framework Description
To promote the practical use of robotic RL, a new software framework known as SERL—Sample-Efficient Robotic reinforcement Learning—has been introduced. This comprehensive suite addresses several core requirements for implementing RL on robots: sample efficiency, seamless incorporation of auxiliary data, setting up reward functions, and enabling environment resets. Central to SERL is a highly efficient off-policy deep RL method that is particularly designed for real-world tasks necessitating image-based inputs.
Key Components and Methodology
SERL's RL algorithm harnesses the emphases of sample-efficient learning, integrating a sought-after balance between data-driven action and computational tractability. It leverages a high update-to-data ratio, symmetric sampling from both prior data and online replay buffers, alongside layer-norm regularization during policy training. This facilitates a smooth commencement from initial demonstrations and a subsequent progression with incremental experiential learning.
Reward functions are inferred using either binary classification methods or adversarial schemes (VICE). SERL also supports the training of forward-backward controllers, which negates the need for manual resets by teaching the robot both task completion and its requisite reset procedure. This is complemented by a robust controller design, apt for the delicate mediation of contact-laden tasks.
Experimental Validation and Performance
The framework's potency is empirically validated across a variety of manipulation tasks demonstrating impressive results. Using only a small number of demonstrations, SERL achieves near-perfect success rates across board insertion, cable routing, and object relocation tasks with surprisingly efficient training times averaging 25 to 50 minutes per policy. This indicates that careful implementation and choice of components can dramatically influence efficiency and opens up pathways to practical application.
Conclusion
SERL establishes a new calibre of efficiency and practicality in robotic RL with an implementation that bridges the gap between theoretical RL advancements and real-world applicability. It underscores the importance of meticulous design in algorithm deployment and sets a precedent for future development in the field, potentially lowering the barriers for robotics practitioners and researchers alike. The code, documentation, and accompanying videos are made available open-source, hoping that this well-curated tool will spur further robotic RL innovations.
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