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#Hybrid Expert (2)

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MoE-Loco: Mixture of Experts for Multitask Locomotion

MoE-Loco: Mixture of Experts for Multitask Locomotion

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.

Runhan Huang, Shaoting Zhu, Yilun DuMar 11, 2025
Hybrid ExpertMulti-Task WalkingLegged robotsMar 11, 2025
Emergent Compositional Skills in Mixture-of-Experts VLAs

Emergent Compositional Skills in Mixture-of-Experts VLAs

We consider the problem of learning compositional robot policies end-to-end from expert demonstrations, without any pre-specified notion of task decomposition or hierarchy. We ask whether a VLA trained with a simplified Mixture-of-Experts (MoE) action head can emergently learn to decompose tasks into reusable, interpretable primitives. We find that learned experts are heavily reused across tasks and consistently correspond to qualitatively distinct low-level behaviors, suggesting that the router implicitly learns to perform high-level sequencing while experts serve as compositional primitives. Our MoE matches the task performance of a monolithic baseline while demonstrating meaningful expert specialization, a step toward modular, interpretable robot policies that emerge from data alone.

Shlok Shah, Rhiaan Jhaveri, Tharun Kumar Tiruppali KalidossJul 22, 2026
VLAHybrid ExpertCompositional GeneralizationJul 22, 2026