Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots

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.

Publication Details

Journal
ArXiv.org
Published
2025-03-07
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
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article

Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots

ArXiv.org
Reinforcement Learning in Robotics
article

Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots

article en

Abstract

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.

ArXiv.org
Openalex Percentile: Top 73%
Reinforcement Learning in Robotics
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Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots · ArXiv.org (2025) | TGRS Research Map | TGRS