CTS-MoE: 基于混合专家模型的隐式地形适应感知运动
CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion
AI总结：
针对非连续地形上的感知运动问题，提出CTS-MoE方法，通过密集混合专家策略与感知门控组合共享行为，并用多批评家防止价值干扰，实现端到端训练和隐式地形适应，在仿真和硬件上优于基线。
摘要：
在不连续地形（如楼梯、间隙和障碍物）上的感知腿式运动需要自适应行为，因为单一的保守步态无法产生应对突然拓扑变化所需的预期动作。将该问题视为多任务强化学习，会在共享与分离之间引入张力。任务使用共同的运动基础但具有冲突的奖励，因此策略必须共享行为同时避免价值干扰。先前的工作只解决了其中一方面：整体策略牺牲了专业化，而分层子策略牺牲了跨过渡和未知地形的泛化能力。我们提出CTS-MoE，它结合了密集混合专家执行器与基于感知的门控来组合共享行为，以及具有任务特定价值头的多批评家来防止干扰。该模型在单阶段并发教师-学生设置中进行端到端训练，处理部分可观测性并避免顺序蒸馏，任务标签仅在训练期间使用。部署时，路由仅依赖于感知，从而无需高层选择器或地形分类器即可实现地形适应。在仿真和硬件上对Unitree Go1进行的实验（涵盖已知和未知地形）显示了任务感知的专业化，与整体基线相比，跟踪误差更低，成功率更高。项目网站：此https URL。
英文摘要：
Perceptive legged locomotion over discontinuous terrain (e.g., stairs, gaps, and obstacles) requires adaptive behavior, as a single conservative gait cannot produce the anticipatory maneuvers needed for abrupt topology changes. Cast as multi-task reinforcement learning, this problem introduces a tension between sharing and separation. Tasks use a common locomotion base but have conflicting rewards, so a policy must share behavior while avoiding value interference. Prior work addresses only one side, with monolithic policies sacrificing specialization and hierarchical sub-policies sacrificing generalization across transitions and unseen terrain. We propose CTS-MoE, which combines a dense mixture-of-experts actor with perception-based gating to compose shared behaviors and a multi-critic with task-specific value heads to prevent interference. The model is trained end-to-end in a single-stage concurrent teacher-student setup that handles partial observability and avoids sequential distillation, with task labels used only during training. At deployment, routing depends solely on perception, allowing terrain adaptation without a high-level selector or terrain classifier. Experiments on a Unitree Go1 in simulation and on hardware across seen and unseen terrains show task-aware specialization, with lower tracking error and higher success rates than monolithic baselines. Project Website: https://cts-moe.github.io/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
