To Select or not to Select, that is the Question: Distilling Robot Skill Prediction into a Small Ensemble
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| Format: | Preprint |
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2026
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| _version_ | 1866913149740908544 |
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| author | Bong, Haechan Mark Roy, Simon Aman, Euhid Beltrame, Giovanni |
| author_facet | Bong, Haechan Mark Roy, Simon Aman, Euhid Beltrame, Giovanni |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_21242 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | To Select or not to Select, that is the Question: Distilling Robot Skill Prediction into a Small Ensemble Bong, Haechan Mark Roy, Simon Aman, Euhid Beltrame, Giovanni Robotics 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. |
| title | To Select or not to Select, that is the Question: Distilling Robot Skill Prediction into a Small Ensemble |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.21242 |