RSL-RL: A Learning Library for Robotics Research

作者：Clemens Schwarke, Mayank Mittal, N. Rudin, David Hoeller, Marco Hutter · 发表于：arXiv.org · 年份：2025 · DOI：10.48550/arxiv.2509.10771 · 被引用次数：88 · 研究领域：Computer Science

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.