arXiv:2606.17294v1 Announce Type: cross Abstract: Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments. To maintain generality, many vision-based navigation models predict normalized actions. However, this normalization introduces a critical deployment vulnerability: applying different scaling factors to the same normalized trajectory alters its physical geometry, which degrades navigation performance and increases collision risks. We address this vulnerability by conditioning the m
The proliferation of Vision Navigation Foundation Models (VNMs) demands more robust and adaptable deployment solutions, highlighting the current challenge of scale awareness.
This development addresses a critical vulnerability in autonomous navigation, enabling safer and more reliable zero-shot deployment of AI-powered systems across varied environments.
Vision-based navigation models can now maintain physical geometry and performance consistency regardless of scaling factors, reducing collision risks in real-world applications.
- · Robotics companies
- · Logistics and delivery sectors
- · Autonomous vehicle developers
- · AI research institutions
- · Companies with less robust navigation systems
- · Traditional, non-adaptive navigation solutions
Improved reliability and safety of autonomous systems in complex environments.
Accelerated adoption and scaling of robot deployments in diverse industrial and commercial applications.
Enhanced trust in autonomous technologies, potentially leading to broader societal integration of AI-driven robotics.
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Read at arXiv cs.LG