City-scale outdoor navigation is currently hindered by the heavy reliance on dense maps or costly navigation supervision. In this work, we introduce a novel paradigm for leveraging directional instructions from commercial navigation tools (e.g., Google Maps). To bridge the gap between commercial instructions and executable navigation actions, while mitigating long-horizon error accumulation through robust trajectory recovery, we propose DA-Nav, a Direction-Aware vision-language Navigation framework that reformulates navigation as a discrete spatial grounding problem on the egocentric 2D image plane. To achieve trajectory recovery, DA-Nav employs a Chain-of-Thought (CoT) reasoning process encompassing deviation assessment, action prediction, and target grid selection. We further introduce ReDA, a dataset that provides direction-aware instructions and recovery trajectories to enhance spatial grounding and support CoT recovery reasoning. Extensive experiments in CARLA demonstrate that DA-Nav achieves a high success rate of 56.16% in unseen urban environments, outperforming existing State-of-The-Art (SoTA) methods while maintaining a substantially stronger recovery capability. Furthermore, without fine-tuning, DA-Nav seamlessly adapts to both quadruped and humanoid robots, enabling stable kilometer-scale closed-loop outdoor navigation in complex real world environments.
Ye Yuan, Kehan Chen, Xinqiang Yu et al.· 0 citations
Prompt-based learning has emerged as a promising paradigm for Class Incremental Learning (CIL), enabling pre-trained models to adapt efficiently to open-world scenarios. Existing methods often employ correlation-based strategies, where an image's feature serves as a query to retrieve the most relevant key prompts, with corresponding value prompts for training. However, these approaches face a fundamental challenge: fitting the entire feature space of all tasks with only a few trainable prompts severely limits the pre-trained model's retrieval capability. In this paper, we propose Predictive Prompting (PrePrompt), a novel CIL framework that circumvents correlation-based limitations by leveraging the inherent classification ability of pre-trained models to predict task-specific prompts. Specifically, PrePrompt decomposes CIL into a two-stage prediction process: task-specific prompt prediction followed by a label prediction. While theoretically sound, this framework risks bias toward recent classes due to missing historical information for calibrating older classifiers. To mitigate this, PrePrompt incorporates a feature extrapolation technique, dynamically balancing stability and plasticity across classifiers. Extensive experiments on several benchmarks demonstrate PrePrompt's superiority over state-of-the-art prompt-based CIL methods. Code is available at https://github.com/libo-huang/preprompt.
Libo Huang, Xiangqi Li, Jiarui Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
RSIAT significantly outperforms state-of-the-art methods in both performance and parameter efficiency, achieving superior stability–plasticity trade-offs with minimal trainable parameters.
Jiarui Zhao, Libo Huang, Xiangqi Li et al.· 0 citations