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RoboRouter: Training-Free Policy Routing for Robotic Manipulation
RoboRouter proposes a training-free policy routing framework for robotic manipulation. Instead of pursuing a single universal policy, it selects a more suitable policy for each manipulation task from an existing…
Summary
RoboRouter proposes a training-free policy routing framework for robotic manipulation. Instead of pursuing a single universal policy, it selects a more suitable policy for each manipulation task from an existing heterogeneous policy pool. Its core idea is to use historical execution experience, similar-task retrieval, and post-execution feedback to consistently outperform any individual policy in both simulation and real-robot settings.
Problem
- The robotic manipulation field already includes multiple policy paradigms such as VLA, VA, and code-based composition, but each is strong on specific tasks and weak in cross-task generalization; no single method wins comprehensively.
- Running all candidate policies and relying on trial and error each time is too costly; meanwhile, training a new router model would introduce the costs of data, training, and continuously integrating new policies.
- This matters because robotic systems are rapidly accumulating large numbers of off-the-shelf policies; without efficient ways to combine these “experts,” system capability and scalability will be constrained.
Approach
- Maintain a heterogeneous policy pool and a historical execution database; when facing a new task, do not retrain, but first build a multimodal task representation (instruction, image, and metadata extracted by vision models).
- Use vector retrieval to find similar historical tasks, then let an LLM-style Router directly predict the policy most likely to succeed for the current task based on those records and each policy’s overall performance.
- After execution, a VLM-style Evaluator reads video frames and standardized metrics (success flag, elapsed time, distance, contact/alignment, etc.) to generate structured feedback; the Recorder writes this information back into the database and routing context, forming an online closed-loop improvement process.
- When a new policy is added, only lightweight evaluation on a small number of representative tasks is needed before writing it into memory, with no additional training required; task clustering further reduces evaluation overhead.
Results
- Simulation (RoboTwin 2.0): Across 20 representative tasks with 100 trials per task, RoboRouter achieves an average success rate of 79.85%, outperforming all single-policy baselines: ACT 55.90%, DP 51.05%, DP3 76.45%, RDT 60.35%, (\pi_0) 69.90%, and Code as Policies 54.45%.
- According to the paper abstract, RoboRouter improves average success rate by more than 3% over individual policies in simulation; from Table 1, compared with the strongest single average baseline DP3 (76.45%), the gain is about 3.40 percentage points.
- Real robot: Across 5 tasks with 20 trials per task, RoboRouter reaches an average success rate of 47%, higher than ACT 26%, DP 18%, RDT 33%, and (\pi_0) 34%.
- According to the paper abstract, the average success rate improvement in the real world is more than 13%; from Table 2, compared with the strongest single average baseline (\pi_0) (34%), the gain is about 13 percentage points.
- Specific real-task examples: on Click Alarmclock, RoboRouter scores 10/20, outperforming RDT 8/20 and (\pi_0) 8/20; on Open Laptop, RoboRouter scores 9/20, better than the best single baseline at 8/20; on Place Container Plate, RoboRouter scores 12/20, tying the best baseline.
- The paper also claims that the additional latency introduced by routing is small and execution efficiency is largely preserved, but the timing table in the provided excerpt is truncated, so no complete, verifiable numerical values are provided.
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