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Multi-Tool Selection for Robotic Systems: Comparing Semantic Retrieval and LLM — arXiv:2610.31013

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31013
License (as archived): CC0-1.0
Archived: 2026-10-25T08:34:47.945Z
SHA-256 of the archived text: 8ed0e40882497ac9abeaeef159305c5eae1a462763eb0df54b9176cf1360e14f
Stable URL of this page: https://metraipolis.com/documento/dc_16bbce476ee7

How to cite

Multi-Tool Selection for Robotic Systems: Comparing Semantic Retrieval and LLM — arXiv:2610.31013 — arXiv (en), https://arxiv.org/abs/2610.31013 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_16bbce476ee7 (Metraipolis archive dc_16bbce476ee7, sha256 8ed0e40882497ac9abeaeef159305c5eae1a462763eb0df54b9176cf1360e14f)

BibTeX

@misc{dc16bbce476ee7,
  title = {Multi-Tool Selection for Robotic Systems: Comparing Semantic Retrieval and LLM — arXiv:2610.31013},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31013},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_16bbce476ee7; sha256 8ed0e40882497ac9abeaeef159305c5eae1a462763eb0df54b9176cf1360e14f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31013. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Natural-language interfaces allow users to command robotic systems without interacting directly with mission planners or robotics middleware. Their reliable use depends on robot tool routing, defined here as the selection of the smallest complete subset of predefined executable capabilities required to satisfy a request. This task is particularly difficult for compound instructions, where incomplete selection may omit necessary sensing or monitoring capabilities, while excessive selection may expose irrelevant tools. We compare four routing strategies: three progressively refined ChromaDB-based methods - fixed-threshold retrieval, relative-distance retrieval, and clause-aware aggregated retrieval - and a cloud-hosted 120-billion-parameter language-model baseline. The methods are evaluated on 164 manually annotated prompts organised into three complexity levels, from direct single-tool requests to compound requests requiring at least three tools. Using exact-match accuracy, completeness, precision, a completeness-weighted composite score, and routing latency, CDB-v3 achieves the best macro-average score among the retrieval-only methods, with 0.7822, compared with 0.8869 for the large-model baseline. Although the LLM provides higher routing quality, CDB-v3 exhibits substantially lower latency and can be deployed locally without requiring cloud connectivity. These results identify a quality-latency trade-off between direct large-model selection and clause-aware semantic retrieval for privacy-sensitive, connectivity-constrained, and resource-constrained robotic systems.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.31013 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T08:31:13.216Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).