VLX-Go: Vision-Language Short-Horizon Waypoint Prediction for Embodied Navigation

VLX-Go is a compact vision-language waypoint planner designed to enhance embodied navigation by predicting short-horizon local waypoints based on recent visual frames, current observations, and natural-language instructions. It addresses the challenge of transforming multimodal inputs into actionable navigation targets for robots, focusing on local motion rather than global route planning. This lightweight model operates in a closed-loop system, allowing for dynamic updates and corrections based on real-time observations, making it suitable for tasks like target following and obstacle avoidance. By separating high-level waypoint prediction from low-level control, VLX-Go provides a practical interface for integrating planning with safety checks and simulator feedback, facilitating easier deployment and evaluation in real-world robotic systems. The model is trained using a combination of offline trajectory data and online simulator feedback to enhance its robustness against obstacles and drift. VLX-Go achieves strong performance metrics, notably in navigation success and target tracking, while maintaining a deployable structure for closed-loop systems.

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