RoboRouter: Training-Free Policy Routing for Robotic Manipulation
Yiteng Chen, Zhe Cao, Hongjia Ren, Chenjie Yang, Wenbo Li, Shiyi Wang, Yemin Wang, Li Zhang, Yanming Shao, Zhenjun Zhao, Huiping Zhuang, Qingyao Wu

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…
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Taxonomy
TopicsReinforcement Learning in Robotics · Robot Manipulation and Learning · Multimodal Machine Learning Applications
