SERL is a software suite designed to facilitate sample-efficient robotic reinforcement learning (RL) in real-world scenarios. It provides a comprehensive framework that includes a high-quality RL implementation, reward specification methods, environment reset mechanisms, and a controller suitable for contact-rich tasks. The system is designed to be user-friendly, enabling researchers and practitioners to quickly implement and test RL algorithms for real-world robotic tasks without extensive setup or integration efforts. SERL supports tasks such as PCB board insertion, cable routing, and object relocation, achieving high success rates with minimal training time. The system uses a sample-efficient off-policy RL method, along with techniques for reward specification and environment resets, to enable efficient learning. The framework also includes a forward-backward controller for automatic task resets and an impedance controller for contact-rich manipulation tasks. The core RL algorithm used in SERL is based on RLPD, an off-policy actor-critic method that can incorporate prior data and demonstrations. Reward functions are specified using binary classifiers or VICE, which helps in defining success based on image observations. The system also supports reset-free training through forward-backward controllers, allowing the robot to learn tasks without manual resets. SERL is designed to be compatible with various robotic environments and provides a flexible framework for real-world robotic learning. It includes software components for environment adaptation, actor and learner nodes, and an impedance controller for contact-rich tasks. The system has been tested on tasks such as PCB insertion, cable routing, and object relocation, demonstrating its effectiveness in achieving high success rates with minimal training time. The results show that SERL can achieve near-perfect success rates for these tasks, with training times significantly lower than previous methods. The system's design emphasizes the importance of implementation details in achieving efficient and effective RL in real-world scenarios. SERL provides a ready-made solution for researchers and practitioners, enabling them to focus on developing new algorithms and methodologies for robotic learning. The software is available as an open-source package, with documentation and videos provided for further exploration.SERL is a software suite designed to facilitate sample-efficient robotic reinforcement learning (RL) in real-world scenarios. It provides a comprehensive framework that includes a high-quality RL implementation, reward specification methods, environment reset mechanisms, and a controller suitable for contact-rich tasks. The system is designed to be user-friendly, enabling researchers and practitioners to quickly implement and test RL algorithms for real-world robotic tasks without extensive setup or integration efforts. SERL supports tasks such as PCB board insertion, cable routing, and object relocation, achieving high success rates with minimal training time. The system uses a sample-efficient off-policy RL method, along with techniques for reward specification and environment resets, to enable efficient learning. The framework also includes a forward-backward controller for automatic task resets and an impedance controller for contact-rich manipulation tasks. The core RL algorithm used in SERL is based on RLPD, an off-policy actor-critic method that can incorporate prior data and demonstrations. Reward functions are specified using binary classifiers or VICE, which helps in defining success based on image observations. The system also supports reset-free training through forward-backward controllers, allowing the robot to learn tasks without manual resets. SERL is designed to be compatible with various robotic environments and provides a flexible framework for real-world robotic learning. It includes software components for environment adaptation, actor and learner nodes, and an impedance controller for contact-rich tasks. The system has been tested on tasks such as PCB insertion, cable routing, and object relocation, demonstrating its effectiveness in achieving high success rates with minimal training time. The results show that SERL can achieve near-perfect success rates for these tasks, with training times significantly lower than previous methods. The system's design emphasizes the importance of implementation details in achieving efficient and effective RL in real-world scenarios. SERL provides a ready-made solution for researchers and practitioners, enabling them to focus on developing new algorithms and methodologies for robotic learning. The software is available as an open-source package, with documentation and videos provided for further exploration.