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MoE-Loco: Mixture of Experts for Multitask Locomotion
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MoE-Loco: Mixture of Experts for Multitask Locomotion
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We present MoE-Loco, a Mixture of Experts (MoE) framework for multitask locomotion for legged robots. Our method enables a single policy to handle diverse terrains, including bars, pits, stairs, slopes, and baffles, while supporting quadrupedal and bipedal gaits. Using MoE, we mitigate the gradient conflicts that typically arise in multitask reinforcement learning, improving both training efficiency and performance. Our experiments demonstrate that different experts naturally specialize in distinct locomotion behaviors, which can be leveraged for task migration and skill composition. We further validate our approach in both simulation and real-world deployment, showcasing its robustness and adaptability.
Forward citations
Cited by 9 Pith papers
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SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
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MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
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