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.

翻译：本文提出MoE-Loco，一种用于腿式机器人多任务运动的专家混合框架。该方法使单一策略能够处理包括横杆、坑道、楼梯、斜坡与挡板在内的多种地形，同时支持四足与双足步态。通过采用MoE架构，我们缓解了多任务强化学习中常见的梯度冲突问题，从而提升了训练效率与性能。实验表明，不同专家会自然地专注于特定的运动行为，这一特性可被用于任务迁移与技能组合。我们进一步在仿真与真实场景中验证了该方法的有效性，证明了其鲁棒性与适应性。