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SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
Jianlan Luo, Zheyuan Hu, Charles Xu, You Liang Tan, Jacob Berg, Archit Sharma, Stefan Schaal, Chelsea Finn, Abhishek Gupta, Sergey Levine
TL;DR
Real-world robotic RL remains difficult to adopt because implementation details, rewards, resets, and control complicate deployment. SERL provides an open-source, vertically integrated framework built around sample-efficient off-policy learning and supporting tools. Across manipulation tasks, it reports efficient training with near-perfect or perfect success rates, while remaining scoped to a selected set of methods and settings.
Problem
Real-world robotic RL is hard to use because implementation choices and physical-system requirements make deployment challenging despite algorithmic progress.
Method
SERL integrates an off-policy RL implementation with demonstration support, image-compatible reward methods, forward-backward resets, robot interfaces, and contact-suitable control.
Results
SERL learns manipulation policies efficiently from image observations, achieving near-perfect or perfect success rates with under an hour of training per policy across reported tasks.
Takeaways & Limitations
The framework provides a practical open-source foundation for real-world robotic RL research and use, including by non-experts.
Takeaways & Limitations
SERL is not a comprehensive library for every RL method or setting, and broad reward specification and reset-free learning remain open problems.
Abstract
from arXiv · showhide
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/
1. Introduction
Robotic RL has advanced substantially, but real-world adoption remains constrained by implementation complexity. SERL addresses this gap with an open-source framework combining carefully selected learning, reward, reset, and control components.
- Motivation: Real-world robotic RL remains difficult to use because implementation details create a large design space beyond algorithm selection.Practitioners recognize implementation details as potentially as important as the algorithm itself.
- Framework: SERL is an open-source framework designed to facilitate wider adoption of RL in real-world robotics.The framework is explicitly intended as a resource for research and practical use rather than a novel algorithmic contribution.
- Framework: SERL combines image-capable off-policy RL, demonstration support, reward specification methods, forward-backward resets, manipulator integration, and impedance control.These components target practical challenges in reward design, resets, robot control, and contact-rich manipulation.
2. Related Work
SERL differs from general RL libraries by integrating the components needed for physical-robot learning into a single vertical pipeline. It builds on prior methods while targeting accessibility and out-of-the-box real-world use.
- Related systems: Prior robotic RL systems require sample efficiency, onboard perception, and easily specified rewards and resets for real-world deployment.These requirements distinguish physical-robot learning from more general RL software needs.
- SERL's positioning: SERL combines controllers, asynchronous training, reward inference, reset-free learning machinery, and an RL algorithm in a full vertical integration.This contrasts with libraries focused horizontally on implementing many separate RL methods.
- SERL's positioning: SERL builds on RLPD, an off-policy algorithm with a high update-to-data ratio, rather than presenting a library of simulation-oriented RL algorithms.Its scope is physical-robot training, though the package could be adapted for simulation.
- Accessibility: The package aims to help non-experts begin training physical robots in the real world and lower the barrier for new researchers.Its contribution is positioned as an integrated practical resource rather than a benchmark package.
3. Preliminaries and Problem Statement
Real-world robotic RL requires more than optimizing a policy: rewards, resets, and safe low-level control must be operationalized on physical systems. SERL provides ready-made solutions centered on sample-efficient off-policy learning and contact-rich manipulation.
- Problem formulation: A robotic RL task is modeled as an MDP with observations, actions, initial-state distribution, transitions, rewards, and a discount factor.The policy maximizes expected discounted cumulative reward under the initial-state distribution, dynamics, and policy.
- Practical challenges: Sample efficiency is paramount because real-world training consumes costly time, motivating effective off-policy RL methods.The passage identifies training time as a central practical constraint.
- Practical challenges: Image-dependent rewards can be difficult to specify manually, while episodic tasks require mechanical resets to sampled initial states.These challenges extend beyond the core optimization of the policy.
- Practical challenges: Controllers must accurately translate policy actions into robot controls while remaining safe during exploration, especially for contact-rich tasks.Safety is required because RL training may use random actions that physically interact with the environment.
- SERL approach: SERL addresses these issues with sample-efficient off-policy learning, multiple reward-specification options, forward-backward reset learning, and a safe controller for contact-rich tasks.The stated goal is to provide ready-made solutions spanning rewards, resets, and control.
4. Sample Efficient Robotic Reinforcement Learning in the Real-World
SERL combines sample-efficient off-policy learning with reward specification, reset learning, robot interfaces, parallel training infrastructure, and a contact-suitable controller for real-world robotic RL. Its design addresses practical requirements including prior-data use, image-based rewards, autonomous resets, fixed-rate control, and safe contact-rich manipulation.
- SERL provides an accessible software package with efficient vision-based RL algorithms and infrastructure for autonomous real-world learning.The package emphasizes easy use without complex setup or integration across libraries.
- Core RL Algorithm: RLPD: RLPD supplies off-policy actor-critic learning that supports multiple updates per time step, prior data, and continued improvement from online experience.SERL uses high update-to-data ratio training, symmetric sampling between prior and online data, and layer-normalization regularization.
- Reward Specification: SERL supports hand-specified, classifier-based, and VICE rewards for tasks with state-based or image-based success signals.VICE adds policy-visited states as negative classifier examples to address reward exploitation during training.
- Reset Learning: Forward-backward controllers enable reset-free training by simultaneously learning task execution and environment-reset policies.The approach uses two independent RL agents with separate policies, Q-functions, and reward functions.
- Robot Systems and Control: The package supports Gym-like environments, arbitrary robot interfaces, and an impedance controller designed for contact-rich manipulation.The controller hierarchy tracks lower-frequency RL targets with a downstream real-time controller and constrains references to reduce collision risk.
- Software Components: SERL separates actor, learner, and robot-environment processes so action execution, training, and data collection proceed in parallel.Separating actor and learner preserves control frequency for deformable-object and contact-rich tasks while reducing real-world wall-clock training time.
- Impedance Controller for Contact-Rich Tasks: Reference limiting clips dangerous contact-directed targets while allowing fast free-space motion toward the commanded target.The controller logs distinguish commanded, smoothed, clipped, and executed positions during these two cases.
5. Experiments
SERL was evaluated on diverse manipulation tasks using randomized starts, demonstrations, and comparisons against behavioral cloning and prior systems. The learned policies achieved strong success rates, faster execution, and efficient training, including successful replication at another institution.
- Tasks and setup: SERL evaluates PCB insertion, cable routing, and object relocation, including contact-rich, deformable-object, and free-floating manipulation.The PCB and cable tasks randomize initial end-effector poses within fixed regions; object relocation resets above each bin.
- Results: RL policies outperformed BC policies in success rate by 1.7x for Object Relocation, 5x for Cable Routing, and 10x for PCB Insertion.Each task was evaluated over 100 trials.
- Baselines: RL training starts from 20 teleoperated demonstrations, whereas the behavioral-cloning baseline uses 100 high-quality demonstrations.The BC baseline therefore uses five times more demonstrations than RL.
- Results: RL policies were at least 2x faster than BC policies trained with 100 high-quality human demonstrations across all three tasks.The comparison measures the average time required for the robot to succeed.
- Results: The RL policies improved cycle time over the initial human demonstrations by up to 3x despite using five times fewer demonstrations than BC.The authors report that demonstrations alone were insufficient for the cable routing and PCB insertion tasks.
- Comparison to prior systems: The implementation qualitatively matches or exceeds prior state-of-the-art results for similar PCB insertion tasks, although direct comparison is difficult.The authors attribute this comparison to differences in setups, open-source availability, and other discrepancies.
- Reproducibility: A University of Washington replication solved peg insertion in 19 minutes with 100/100 success using 20 initial human demonstrations.The complete hardware and software preparation took less than three hours.
6. Discussion
SERL combines software components intended to make real-world robotic RL more accessible and efficient. Its evaluation covers diverse manipulation tasks, while the authors acknowledge limits in scope and reward and reset methods.
- Contributions: SERL combines reward design, reset automation through forward-backward controllers, and a controller framework for contact-rich manipulation.The package is presented as a carefully designed combination for sample-efficient real-world RL.
- Results: The framework learns diverse manipulation tasks in under an hour per policy from a small number of demonstrations.The authors report qualitative comparison with state-of-the-art manipulation results.
- Limitations: SERL is not intended to provide every possible RL method, and some tasks and settings, including non-manipulation tasks, may lie outside its scope.The authors identify this as a limitation of the framework’s coverage.
- Limitations: Classifier-based rewards and the forward-backward controller may not be appropriate in every setting, leaving broader reward specification and reset-free learning as open problems.The authors state that further research is needed to broaden robotic RL applicability.
7. Appendix
SERL expresses observations and actions relative to each episode's initial end-effector frame, allowing policies to transfer across randomized locations. Homogeneous transformations and an Adjoint mapping connect these relative quantities to the robot's base-frame control interface.
- Each episode samples an initial end-effector frame uniformly within a predefined workspace randomization area.
- Proprioceptive information is expressed relative to the episode's initial end-effector frame rather than the robot's global base frame.
- This relative representation lets the policy operate at new locations when the relative end-effector–target geometry remains consistent.
- The policy receives position and rotation extracted from the homogeneous transformation between the initial and current end-effector frames.
- The policy outputs a 6 DoF twist in its observation frame, while the robot controller requires actions transformed through the Adjoint mapping.