arXiv:2604.17473v4 Announce Type: replace-cross Abstract: Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions. While recent Video Large Language Models(Video-LLMs) have largely advanced VLN, they remain highly susceptible to State Drift in long scenarios. In these cases, the agent's internal state drifts away from the true task execution state, leading to aimless wandering and failure to execute essential maneuvers in the instruction. We attribute this failure to two distinct cognitive deficits: Progress Drift, where
The proliferation of Video Large Language Models (Video-LLMs) has advanced Vision-Language Navigation, but also exposed persistent issues like 'State Drift'.
This research addresses a critical limitation in autonomous AI agents, where state drift can lead to navigation failures, impacting reliability and deployment in complex environments.
Improved methods for addressing 'State Drift' will enhance the robustness and effectiveness of AI agents in real-world navigation tasks, making them more dependable.
- · AI Agent Developers
- · Robotics Industry
- · Logistics and Automation Sectors
- · Developers relying on simplistic navigation models
- · Companies with unreliable autonomous systems
More reliable AI-driven navigation systems for various applications become feasible.
Accelerated development and adoption of AI agents in complex physical environments, reducing human intervention.
Enhanced trust in AI autonomy could lead to broader integration across critical infrastructure and everyday life.
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Read at arXiv cs.AI