RSL-RL: A Learning Library for Robotics Research

• URL: http://arxiv.org/abs/2509.10771v1
• Date: Sat, 13 Sep 2025 01:31:43 GMT
• Title: RSL-RL: A Learning Library for Robotics Research
• Authors: Clemens Schwarke, Mayank Mittal, Nikita Rudin, David Hoeller, Marco Hutter,
• Abstract summary: RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community.<n>Unlike broad general-purpose frameworks, its philosophy prioritizes a compact and easily modifiable, allowing researchers to adapt and extend algorithms with minimal overhead.
• Score: 9.89623087508662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy prioritizes a compact and easily modifiable codebase, allowing researchers to adapt and extend algorithms with minimal overhead. The library focuses on algorithms most widely adopted in robotics, together with auxiliary techniques that address robotics-specific challenges. Optimized for GPU-only training, RSL-RL achieves high-throughput performance in large-scale simulation environments. Its effectiveness has been validated in both simulation benchmarks and in real-world robotic experiments, demonstrating its utility as a lightweight, extensible, and practical framework to develop learning-based robotic controllers. The library is open-sourced at: https://github.com/leggedrobotics/rsl_rl.

Related papers

• What Matters for Simulation to Online Reinforcement Learning on Real Robots [51.77095085120584]
  We investigate what specific design choices enable successful online reinforcement learning on physical robots.<n>We systematically ablate algorithmic, systems, and experimental decisions that are typically left implicit in prior work.<n>We find that some widely used defaults can be harmful, while a set of robust, readily adopted design choices within standard RL practice yield stable learning across tasks and hardware.
  arXiv  Detail & Related papers  (2026-02-23T10:34:15Z)
• Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale [11.166320712764465]
  Vision-Language-Action models (VLAs) replace specialized architectures and task-tailored components of expert policies with large-scale data collection and setup-specific fine-tuning.<n>Traditional robotics software frameworks become a bottleneck, while robot simulations offer only limited support for transitioning from and to real-world experiments.<n>We introduce Robot Control Stack (RCS), a lean ecosystem designed from the ground up to support research in robot learning with large-scale generalist policies.
  arXiv  Detail & Related papers  (2025-09-18T13:12:16Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [82.46975428739329]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment.<n>We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation.<n>These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm [2.2168889407389445]
  Reinforcement learning algorithms provide the potential to enable robots to learn optimal solutions to complete new tasks without reprogramming them. Current state-of-the-art in reinforcement learning relies on fast simulations and parallelization to achieve optimal performance. This report outlines our initial research into the application of deep reinforcement learning on an industrial UR10e robot.
  arXiv  Detail & Related papers  (2023-08-28T15:34:43Z)
• Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments [0.3867363075280544]
  In reinforcement learning (RL) research, simulations enable benchmarks between algorithms. In this paper, we introduce Karolos, a framework developed for robotic applications. The code is open source and published on GitHub with the aim of promoting research of RL applications in robotics.
  arXiv  Detail & Related papers  (2022-12-01T23:14:02Z)
• Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization [73.74371798168642]
  We introduce an open-source modular library, RL4LMs, for optimizing language generators with reinforcement learning. Next, we present the GRUE benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions. Finally, we introduce an easy-to-use, performant RL algorithm, NLPO, that learns to effectively reduce the action space in language generation.
  arXiv  Detail & Related papers  (2022-10-03T21:38:29Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning [78.76052604441519]
  SurRoL is an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK) Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.
  arXiv  Detail & Related papers  (2021-08-30T07:43:47Z)
• RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots [3.249853429482705]
  Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics. This paper introduces the RL STaR platform, and how researchers can use it through a demonstration.
  arXiv  Detail & Related papers  (2020-09-21T03:09:53Z)
• MushroomRL: Simplifying Reinforcement Learning Research [60.70556446270147]
  MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies.
  arXiv  Detail & Related papers  (2020-01-04T17:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.