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CoFL: Continuous Flow Fields for Language-Conditioned Navigation

arXiv – CS AI|Haokun Liu, Zhaoqi Ma, Yicheng Chen, Masaki Kitagawa, Wentao Zhang, Jinjie Li, Moju Zhao|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers present CoFL, a new AI navigation system that uses continuous flow fields to enable robots to navigate based on language commands. The system outperforms existing modular approaches by directly mapping bird's-eye view observations and instructions to smooth navigation trajectories, demonstrating successful zero-shot deployment in real-world experiments.

Key Takeaways

→CoFL introduces an end-to-end approach that maps visual observations and language instructions directly to continuous flow fields for navigation.
→The system eliminates the need for brittle modular components by outputting instantaneous velocities queryable at any 2D location.
→Researchers built a dataset of over 500k BEV image-instruction pairs using Matterport3D and ScanNet for large-scale training.
→CoFL significantly outperforms existing Vision-Language Model-based planners on unseen scenes.
→The system successfully deployed zero-shot in real-world experiments with reliable closed-loop control.

#artificial-intelligence #robotics #navigation #computer-vision #language-models #machine-learning #autonomous-systems #research

Read Original →via arXiv – CS AI

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