用专家混合模型让机器人高效学习多任务操作，只调用所需技能。
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
- 构建可复用的技能专家池，按需激活少量专家组合动作。
- 在仿真和双臂机器人上实现更高成功率与更低推理开销。
- 适合需要快速切换任务的工业机器人场景。
基于扩散模型的策略在机器人操作中表现优异，但扩展到多任务场景时受限于模型规模和示范数据的高昂成本。本文提出技能混合专家策略（SMP），一种基于扩散模型的混合专家架构，通过学习一组正交的紧凑技能基底，并采用粘性路由机制，在每一步仅从少量相关专家中组合动作。变分训练目标支持该设计，推理时自适应激活专家，实现快速采样且无需庞大模型。我们在仿真环境和真实双臂平台上验证了SMP在多任务学习与迁移学习中的表现，结果表明其成功率达更高，推理成本显著低于大型扩散基线。这为可扩展、可迁移的多任务操作提供了一条实用路径：一次性学习通用技能，仅激活所需部分，任务变化时快速适应。
原文摘要 · Abstract (English)
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size and demonstrations. We introduce Skill Mixture-of-Experts Policy (SMP), a diffusion-based mixture-of-experts policy that learns a compact orthogonal skill basis and uses sticky routing to compose actions from a small, task-relevant subset of experts at each step. A variational training objective supports this design, and adaptive expert activation at inference yields fast sampling without oversized backbones. We validate SMP in simulation and on a real dual-arm platform with multi-task learning and transfer learning tasks, where SMP achieves higher success rates and markedly lower inference cost than large diffusion baselines. These results indicate a practical path toward scalable, transferable multi-task manipulation: learn reusable skills once, activate only what is needed, and adapt quickly when tasks change.
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