RoboRouter: Training-Free Policy Routing for Robotic Manipulation

• URL: http://arxiv.org/abs/2603.07892v3
• Date: Thu, 12 Mar 2026 05:45:57 GMT
• Title: RoboRouter: Training-Free Policy Routing for Robotic Manipulation
• Abstract summary: We propose to leverage the complementary strengths of existing approaches through intelligent policy routing.<n>We introduce Robo, a training-free framework that maintains a pool of heterogeneous policies and learns to select the best-performing policy for each task.<n>Our results demonstrate that intelligent routing across heterogeneous, off-the-shelf policies provides a practical and scalable pathway toward building more capable robotic systems.
• Score: 28.477634030948376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA) policies, and code-based compositional approaches. Concrete policies typically attain high success rates on specific task distributions but lim-ited generalization beyond it. Rather than proposing an other monolithic policy, we propose to leverage the complementary strengths of existing approaches through intelligent policy routing. We introduce RoboRouter, a training-free framework that maintains a pool of heterogeneous policies and learns to select the best-performing policy for each task through accumulated execution experience. Given a new task, RoboRouter constructs a semantic task representation, retrieves historical records of similar tasks, predicts the optimal policy choice without requiring trial-and-error, and incorporates structured feedback to refine subsequent routing decisions. Integrating a new policy into the system requires only lightweight evaluation and incurs no training overhead. Across simulation benchmark and real-world evaluations, RoboRouter consistently outperforms than in-dividual policies, improving average success rate by more than 3% in simulation and over 13% in real-world settings, while preserving execution efficiency. Our results demonstrate that intelligent routing across heterogeneous, off-the-shelf policies provides a practical and scalable pathway toward building more capable robotic systems.

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