一键训练仿真机器人，支持多种运动模式。
Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots
- 基于Unity ML-Agents，导入模型即可一键训练
- 支持行走、奔跑、跳跃等多模式运动学习
- 适用于机器人设计优化与形态进化研究
本文介绍Unity RL Playground，一个基于Unity ML-Agents的开源强化学习框架。该框架自动化训练移动机器人完成各类运动任务，如行走、奔跑和跳跃，具备向真实硬件无缝迁移的潜力。核心功能包括：一键训练导入的机器人模型、兼容多种机器人配置、支持多模式运动学习，以及极端性能测试，助力机器人设计优化与形态演化。附带视频可访问https://linqi-ye.github.io/video/iros25.mp4，代码即将发布。
原文摘要 · Abstract (English)
This paper introduces Unity RL Playground, an open-source reinforcement learning framework built on top of Unity ML-Agents. Unity RL Playground automates the process of training mobile robots to perform various locomotion tasks such as walking, running, and jumping in simulation, with the potential for seamless transfer to real hardware. Key features include one-click training for imported robot models, universal compatibility with diverse robot configurations, multi-mode motion learning capabilities, and extreme performance testing to aid in robot design optimization and morphological evolution. The attached video can be found at https://linqi-ye.github.io/video/iros25.mp4 and the code is coming soon.
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