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

The paper presents a vision‑language navigation system that transfers from simulation to a real Ackermann‑steered mobile robot without relying on navigation graphs or panoramic views. It uses a Cross‑Modal Attention architecture trained on simulated data and fine‑tuned with limited real‑world episodes, leveraging linear photometric adjustments and a camera‑LiDAR sensor suite. Evaluation with SPL and nDTW metrics shows robust, adaptable navigation in continuous environments.

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• llms
• rag
• agents
• robotics
• fine-tuning
• multimodal