Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911130832601088 |
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| author | Han, Tyler Shah, Preet Rajagopal, Sidharth Bao, Yanda Jung, Sanghun Talia, Sidharth Guo, Gabriel Xu, Bryan Mehta, Bhaumik Romig, Emma Scalise, Rosario Boots, Byron |
| author_facet | Han, Tyler Shah, Preet Rajagopal, Sidharth Bao, Yanda Jung, Sanghun Talia, Sidharth Guo, Gabriel Xu, Bryan Mehta, Bhaumik Romig, Emma Scalise, Rosario Boots, Byron |
| contents | Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary simulators, expensive hardware, and a daunting range of tools and skills. As a result, broader communities are disconnecting from the state-of-the-art; education curricula are poorly equipped to teach indispensable modern robotics skills involving hardware, deployment, and iterative development. To address this gap between the broader and scientific communities, we contribute Wheeled Lab, an ecosystem which integrates accessible, open-source wheeled robots with Isaac Lab, an open-source robot learning and simulation framework, that is widely adopted in the state-of-the-art. To kickstart research and education, this work demonstrates three state-of-the-art zero-shot policies for small-scale RC cars developed through Wheeled Lab: controlled drifting, elevation traversal, and visual navigation. The full stack, from hardware to software, is low-cost and open-source. Videos and additional materials can be found at: https://uwrobotlearning.github.io/WheeledLab/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics Han, Tyler Shah, Preet Rajagopal, Sidharth Bao, Yanda Jung, Sanghun Talia, Sidharth Guo, Gabriel Xu, Bryan Mehta, Bhaumik Romig, Emma Scalise, Rosario Boots, Byron Robotics Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary simulators, expensive hardware, and a daunting range of tools and skills. As a result, broader communities are disconnecting from the state-of-the-art; education curricula are poorly equipped to teach indispensable modern robotics skills involving hardware, deployment, and iterative development. To address this gap between the broader and scientific communities, we contribute Wheeled Lab, an ecosystem which integrates accessible, open-source wheeled robots with Isaac Lab, an open-source robot learning and simulation framework, that is widely adopted in the state-of-the-art. To kickstart research and education, this work demonstrates three state-of-the-art zero-shot policies for small-scale RC cars developed through Wheeled Lab: controlled drifting, elevation traversal, and visual navigation. The full stack, from hardware to software, is low-cost and open-source. Videos and additional materials can be found at: https://uwrobotlearning.github.io/WheeledLab/ |
| title | Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.07380 |