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arXiv · 2605.13058

MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots

Yuqi Li·Peng Zhai·Yueqi Zhang·Xiaoyi Wei·Quancheng Qian·Zhengxu He·Qianxiang Yu·Lihua Zhang
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Abstract

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.

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Yuqi Li, Peng Zhai, Yueqi Zhang, Xiaoyi Wei, Quancheng Qian, Zhengxu He, Qianxiang Yu, Lihua Zhang. 2026-05-13. MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots. https://arxiv.org/abs/2605.13058

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