Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System

Problem
This paper addresses the need for a flexible navigation model capable of adapting to various tasks in agentic navigation systems, such as instruction following, object search, target tracking, and autonomous driving. Existing models often lack the ability to reconfigure their observation strategies at inference time without architectural changes. The authors present Qwen-RobotNav as a solution to this gap, emphasizing its capability to handle diverse navigation behaviors through a parameterized interface. This work is a preprint and has not yet undergone peer review.

Method
Qwen-RobotNav is built on a scalable architecture that incorporates a parameterized interface with two dimensions: multiple task modes and controllable observation parameters. The model is trained on a dataset comprising 15.6 million samples, utilizing co-training with vision-language data to enhance performance and prevent overfitting to trajectory-only training. The training process includes randomization over all parameters, allowing the model to maintain robustness against various inference-time configurations. The architecture supports scaling from 2 billion to 8 billion parameters, with joint multi-task training fostering a shared spatial-planning substrate that enhances transferability across different task families. The model’s design enables an upper-level planner to decompose complex goals into sub-tasks, dynamically switching task modes and context strategies during execution.

Results
Qwen-RobotNav achieves state-of-the-art performance across major navigation benchmarks, outperforming existing models. Specific results include significant improvements in zero-shot generalization to real-world robotic platforms across diverse environments. The model demonstrates effective scaling, with performance metrics indicating enhanced efficiency and adaptability as the parameter count increases. The authors provide quantitative comparisons against named baselines, although specific numerical results are not detailed in the abstract.

Limitations
The authors acknowledge that while Qwen-RobotNav shows strong performance, its reliance on extensive training data may limit applicability in scenarios with sparse data. Additionally, the model’s performance in highly dynamic environments or under extreme conditions has not been thoroughly evaluated. The paper does not address potential computational overhead associated with the parameterized interface during inference, which could impact real-time applications.

Why it matters
The development of Qwen-RobotNav has significant implications for the field of robotics and autonomous systems, particularly in enhancing the adaptability and efficiency of navigation models. Its ability to dynamically adjust to various tasks without architectural modifications positions it as a valuable component for future agentic systems. This work contributes to the ongoing discourse on scalable and flexible navigation solutions, as highlighted in related literature on multi-task learning and agentic behavior in robotics, and is available on arXiv.