SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

2024-01-29

IEEE International Conference on Robotics and Automation

AI Summary

Existing robotic reinforcement learning (RL) methods suffer from sensitivity to implementation details and poor accessibility, hindering real-world deployment. This paper introduces the first sample-efficient offline RL software suite designed explicitly for real hardware, integrating reward shaping, robust environment reset mechanisms, high-precision closed-loop control, and multi-task benchmarks. We adopt an enhanced Soft Actor-Critic (SAC) framework augmented with joint vision-action representation learning and an end-to-end, reproducible industrial-grade implementation paradigm. Our approach achieves policy training in under one hour (25–50 minutes) on physical robots—the first such demonstration—and exhibits emergent behaviors including recovery and error correction. It attains near-perfect success rates (~100%) on PCB assembly, cable routing, and object repositioning tasks, while demonstrating strong robustness to external disturbances. Training efficiency significantly surpasses state-of-the-art methods.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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 the widespread adoption of robotic RL, as well as the 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 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/

Problem

• Addresses inaccessibility of robotic reinforcement learning methods.
• Provides a sample-efficient off-policy deep RL library.
• Enables efficient learning for complex robotic tasks.

Innovation

• Sample-efficient off-policy deep RL method
• High-quality controller for widely-adopted robot
• Efficient learning for complex robotic tasks