港大强化学习驱动连续环境具身导航方法：VLN-R1

Core Viewpoint - The article presents the VLN-R1 framework, which utilizes large vision-language models (LVLM) for continuous navigation in real-world environments, addressing limitations of previous discrete navigation methods [5][15]. Research Background - The VLN-R1 framework processes first-person video streams to generate continuous navigation actions, enhancing the realism of navigation tasks [5]. - The VLN-Ego dataset is constructed using the Habitat simulator, providing rich visual and language information for training LVLMs [5][6]. - The importance of visual-language navigation (VLN) is emphasized as a core challenge in embodied AI, requiring real-time decision-making based on natural language instructions [5]. Methodology - The VLN-Ego dataset includes natural language navigation instructions, historical frames, and future action sequences, designed to balance local details and overall context [6]. - The training method consists of two phases: supervised fine-tuning (SFT) to align action predictions with expert demonstrations, followed by reinforcement fine-tuning (RFT) to optimize model performance [7][9]. Experimental Results - In the R2R task, VLN-R1 achieved a success rate (SR) of 30.2% with the 7B model, significantly outperforming traditional models without depth maps or navigation maps [11]. - The model demonstrated strong cross-domain adaptability, outperforming fully supervised models in the RxR task with only 10K samples used for RFT [12]. - The design of predicting future actions was found to be crucial for performance, with the best results obtained by predicting six future actions [14]. Conclusion and Future Work - VLN-R1 integrates LVLM and reinforcement learning fine-tuning, achieving state-of-the-art performance in simulated environments and showing potential for small models to match larger ones [15]. - Future research will focus on validating the model's generalization capabilities in real-world settings and exploring applications in other embodied AI tasks [15].