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Sim-to-Real Transfer of Vision-Language Navigation in Continuous Environments Using an Ackermann-Steered Mobile Robot — arXiv:2610.07192

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07192
License (as archived): CC0-1.0
Archived: 2026-10-07T11:18:24.703Z
SHA-256 of the archived text: 4c7d98c513ba2e1590041071e81fe52d7552c9b938b8744705ff7f31c21d598f
Stable URL of this page: https://metraipolis.com/documento/dc_cd67ba2178f2

How to cite

Sim-to-Real Transfer of Vision-Language Navigation in Continuous Environments Using an Ackermann-Steered Mobile Robot — arXiv:2610.07192 — arXiv (en), https://arxiv.org/abs/2610.07192 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd67ba2178f2 (Metraipolis archive dc_cd67ba2178f2, sha256 4c7d98c513ba2e1590041071e81fe52d7552c9b938b8744705ff7f31c21d598f)

BibTeX

@misc{dccd67ba2178f2,
  title = {Sim-to-Real Transfer of Vision-Language Navigation in Continuous Environments Using an Ackermann-Steered Mobile Robot — arXiv:2610.07192},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07192},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd67ba2178f2; sha256 4c7d98c513ba2e1590041071e81fe52d7552c9b938b8744705ff7f31c21d598f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07192. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision-Language Navigation (VLN) enables robots to navigate through environments using natural language instructions, making human-robot interaction intuitive. Traditional VLN models often rely on navigation graphs, 360-degree views, and perfect localization which pose significant challenges when adapting these models to real-world settings. This work addresses these limitations by performing a simulation-to-real domain shift of a VLN approach that operates in continuous environments without requiring navigation graphs or panoramic views. The proposed system integrates vision-language models that align visual inputs and linguistic instructions within a shared embedding space, facilitating natural language-driven navigation. We employ a Cross-Modal Attention (CMA) based architecture trained on an existing dataset in a simulated environment and fine-tune it using real-world data collected from a custom-built Ackermann-steered robot equipped with a camera and a LiDAR sensor. By utilising linear photometric adjustments and fine-tuning on a limited number of episodes, our model successfully adapts to real-world environments, achieving effective navigation while running offline on dedicated hardware. Experimental results, evaluated using Success weighted by Path Length (SPL) and Normalized Dynamic Time Warping (nDTW) metrics, demonstrate the robustness and adaptability of our approach. Keywords: Vision-Language Navigation, Cross-Modal Attention, Natural Language Instructions, Sim-to-Real Transfer, Autonomous Navigation, Ackermann-steering.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.07192 — 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 2026-10-07T11:15:24.482Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).