端到端强化学习框架让机器人轻松实现复杂行走。
Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion
- 从训练到部署全流程整合，支持多种强化学习方法。
- 在真实机器人上实现全向走、抗扰动和地形适应能力。
- 开源代码助力社区快速开发人形机器人运动策略。
近年来强化学习在人形机器人行走方面取得显著进展，简化了仿真环境中的运动策略设计与训练。然而，众多实现细节使得将策略迁移到真实机器人面临挑战。为此，我们开发了一个涵盖训练到部署全过程的完整代码框架，集成常见强化学习训练方法、领域随机化、奖励函数设计及并行结构处理方案。该库作为社区资源公开，附有详细设计说明与实验结果。我们在 Booster T1 机器人上验证了该框架，证明训练策略可无缝迁移至物理平台，实现全向行走、抗干扰与地形适应等能力。我们希望本工作为机器人领域提供便捷工具，加速人形机器人研发进程。代码见 https://github.com/BoosterRobotics/booster_gym。
原文摘要 · Abstract (English)
Recent advancements in reinforcement learning (RL) have led to significant progress in humanoid robot locomotion, simplifying the design and training of motion policies in simulation. However, the numerous implementation details make transferring these policies to real-world robots a challenging task. To address this, we have developed a comprehensive code framework that covers the entire process from training to deployment, incorporating common RL training methods, domain randomization, reward function design, and solutions for handling parallel structures. This library is made available as a community resource, with detailed descriptions of its design and experimental results. We validate the framework on the Booster T1 robot, demonstrating that the trained policies seamlessly transfer to the physical platform, enabling capabilities such as omnidirectional walking, disturbance resistance, and terrain adaptability. We hope this work provides a convenient tool for the robotics community, accelerating the development of humanoid robots. The code can be found in https://github.com/BoosterRobotics/booster_gym.
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