RoboRouter: Training-Free Policy Routing for Robotic Manipulation

TL;DR

RoboRouter is a training-free framework that intelligently routes among diverse robotic manipulation policies based on task similarity, significantly improving success rates without additional training.

Contribution

It introduces a novel, training-free policy routing system that leverages existing policies and structured feedback to enhance robotic manipulation performance.

Findings

Over 3% success rate improvement in simulation.

Over 13% success rate improvement in real-world.

Efficient policy selection without additional training.

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.