RESEARCH · RESEARCH · #1633
Sim-to-real transfer for Vision-Language Navigation using Cross-Modal Attention on an Ackermann-steered robot (arXiv:2610.07192v1)
This arXiv v1 paper presents a VLN system that operates in continuous environments without navigation graphs or panoramic views, using a Cross-Modal Attention architecture trained in simulation and fine-tuned on limited real-world episodes collected with a custom Ackermann-steered robot outfitted with a camera and LiDAR. The authors apply linear photometric adjustments and demonstrate sim-to-real adaptation with evaluations reported via SPL and nDTW, running offline on dedicated hardware.
KEY POINTS
- This arXiv v1 paper presents a VLN system that operates in continuous environments without navigation graphs or panoramic views, using a Cross-Modal Attention architecture trained in simulation and fine-tuned on limited real-world episodes collected with a custom Ackermann-steered robot outfitted with a camera and LiDAR.
- The authors apply linear photometric adjustments and demonstrate sim-to-real adaptation with evaluations reported via SPL and nDTW, running offline on dedicated hardware.
- Shows a practical sim-to-real approach for VLN in continuous, non‑panoramic settings using few real episodes and an Ackermann platform, which is relevant for deploying language-guided navigation on real mobile robots.
WHY IT MATTERS
Shows a practical sim-to-real approach for VLN in continuous, non‑panoramic settings using few real episodes and an Ackermann platform, which is relevant for deploying language-guided navigation on real mobile robots.