MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918499498065920 |
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| author | Li, Yuqi Zhai, Peng Zhang, Yueqi Wei, Xiaoyi Qian, Quancheng He, Zhengxu Yu, Qianxiang Zhang, Lihua |
| author_facet | Li, Yuqi Zhai, Peng Zhang, Yueqi Wei, Xiaoyi Qian, Quancheng He, Zhengxu Yu, Qianxiang Zhang, Lihua |
| contents | Wheeled-legged robots hold promise for traversing complex terrains and offer superior mobility compared to legged robots. However, wheeled-legged robots must effectively balance both wheeled driving and legged control. Furthermore, due to noisy proprioceptive sensing and real-world motor constraints, realizing robust and adaptive locomotion at peak performance of motors remains challenging. We propose the Multi-skill Unified Joint Integration of Control Architecture (MUJICA), a unified, fully proprioceptive control framework for wheeled-legged robots that integrates diverse low-level skills-including omnidirectional moving, high platform climbing, and fall recovery-within a single policy. All skills, distinguished by unique indicator variables, are trained jointly with accurate DC-motor constraint modeling. Additionally, a high-level skill selector is learned to dynamically choose the optimal skill based solely on proprioceptions, enabling adaptive responses to the surrounding environment. Therefore, MUJICA enhances sim-to-real robustness and enables seamless transitions across diverse locomotion modes, facilitating autonomous adjustment to the environment. We validate our framework in both simulation and real-world experiments on the Unitree Go2-W robot, demonstrating significant improvements in adaptability and task success in unstructured environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13058 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots Li, Yuqi Zhai, Peng Zhang, Yueqi Wei, Xiaoyi Qian, Quancheng He, Zhengxu Yu, Qianxiang Zhang, Lihua Robotics Wheeled-legged robots hold promise for traversing complex terrains and offer superior mobility compared to legged robots. However, wheeled-legged robots must effectively balance both wheeled driving and legged control. Furthermore, due to noisy proprioceptive sensing and real-world motor constraints, realizing robust and adaptive locomotion at peak performance of motors remains challenging. We propose the Multi-skill Unified Joint Integration of Control Architecture (MUJICA), a unified, fully proprioceptive control framework for wheeled-legged robots that integrates diverse low-level skills-including omnidirectional moving, high platform climbing, and fall recovery-within a single policy. All skills, distinguished by unique indicator variables, are trained jointly with accurate DC-motor constraint modeling. Additionally, a high-level skill selector is learned to dynamically choose the optimal skill based solely on proprioceptions, enabling adaptive responses to the surrounding environment. Therefore, MUJICA enhances sim-to-real robustness and enables seamless transitions across diverse locomotion modes, facilitating autonomous adjustment to the environment. We validate our framework in both simulation and real-world experiments on the Unitree Go2-W robot, demonstrating significant improvements in adaptability and task success in unstructured environments. |
| title | MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.13058 |