To Select or not to Select, that is the Question: Distilling Robot Skill Prediction into a Small Ensemble
For roboticists managing heterogeneous fleets, this work provides a practical, efficient alternative to large LLMs for task routing, though the approach is incremental.
The authors tackle robot skill prediction for heterogeneous fleets, constructing a synthetic dataset via LLM assistance and achieving 83.5% task-to-skill matching with a small ensemble, outperforming much larger LLMs like Kimi K2 (72.0%) and GPT-OSS-120B (71.5%).
As robot fleets become more heterogeneous, including humanoids, rovers, quadrupeds, and drones, selecting the right robot for a task becomes a core systems problem. We study robot skill prediction: mapping a natural-language task description to the physical capabilities required to execute it, such as fly, wheels, legs, surface water, under water and hands. Since labelled data that maps natural-language task descriptions to robot's physical capabilities does not exist, we construct a synthetic task-to-skill dataset using LLM-assisted generation and targeted label auditing. Trained on this data, a ~133M-parameter ensemble of two fine-tuned sentence encoders (mpnet + MiniLM) reaches 83.5% task-to-skill matching on a stratified 200 task dataset, outperforming Kimi K2 (1T MoE) at 72.0%, GPT-OSS-120B at 71.5%, and Llama-4-Scout-17B at 69.0% under the same zero-shot prompt. These results suggest that, for fixed robot skill taxonomies, small specialized models trained on synthetic data can outperform much larger general-purpose LLMs for fleet-level task routing.