Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies

• URL: http://arxiv.org/abs/2601.21251v1
• Date: Thu, 29 Jan 2026 04:17:56 GMT
• Title: Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
• Abstract summary: 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.<n>We introduce Skill Mixture-of-Experts Policy (SMP), a diffusion-based mixture-of-experts policy that learns a compact skill basis and uses sticky routing to compose actions from a small, task-relevant subset of experts at each step.<n>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.
• Score: 13.826293542795938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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