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Qunbo Wang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

UrbanNav: Learning Language-Guided Embodied Urban Navigation from Web-Scale Human Trajectories

Yanghong Mei
Yirong Yang
Longteng Guo
Qunbo Wang
Ming-Ming Yu
Xingjian He
Wenjun Wu
Jing Liu

Navigating complex urban environments using natural language instructions poses significant challenges for embodied agents, including noisy language instructions, ambiguous spatial references, diverse landmarks, and dynamic street scenes. Current visual navigation methods are typically limited to simulated or off-street environments, and often rely on precise goal formats, such as specific coordinates or images. This limits their effectiveness for autonomous agents like last-mile delivery robots navigating unfamiliar cities. To address these limitations, we introduce UrbanNav, a scalable framework that trains embodied agents to follow free-form language instructions in diverse urban settings. Leveraging web-scale city walking videos, we develop an scalable annotation pipeline that aligns human navigation trajectories with language instructions grounded in real-world landmarks. UrbanNav encompasses over 1,500 hours of navigation data and 3 million instruction-trajectory-landmark triplets, capturing a wide range of urban scenarios. Our model learns robust navigation policies to tackle complex urban scenarios, demonstrating superior spatial reasoning, robustness to noisy instructions, and generalization to unseen urban settings. Experimental results show that UrbanNav significantly outperforms existing methods, highlighting the potential of large-scale web video data to enable language-guided, real-world urban navigation for embodied agents.

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NeurIPS Conference 2025 Conference Paper

C-NAV: Towards Self-Evolving Continual Object Navigation in Open World

MingMing Yu
Fei Zhu
Wenzhuo Liu
Yirong Yang
Qunbo Wang
Wenjun Wu
Jing Liu

Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of object categories during training, overlooking the real-world requirement for continual adaptation to evolving scenarios. To facilitate related studies, we introduce the continual object navigation benchmark, which requires agents to acquire navigation skills for new object categories while avoiding catastrophic forgetting of previously learned knowledge. To tackle this challenge, we propose C-Nav, a continual visual navigation framework that integrates two key innovations: (1) A dual-path anti-forgetting mechanism, which comprises feature distillation that aligns multi-modal inputs into a consistent representation space to ensure representation consistency, and feature replay that retains temporal features within the action decoder to ensure policy consistency. (2) An adaptive sampling strategy that selects diverse and informative experiences, thereby reducing redundancy and minimizing memory overhead. Extensive experiments across multiple model architectures demonstrate that C-Nav consistently outperforms existing approaches, achieving superior performance even compared to baselines with full trajectory retention, while significantly lowering memory requirements. The code will be publicly available at \url{https: //bigtree765. github. io/C-Nav-project}.

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NeurIPS Conference 2023 Conference Paper

VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset

Sihan Chen
Handong Li
Qunbo Wang
Zijia Zhao
Mingzhen Sun
Xinxin Zhu
Jing Liu

Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and Text by exploring an automatically generated large-scale omni-modality video caption dataset called VAST-27M. Specifically, we first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. Then, we employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions. Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA). Extensive experiments have been conducted to demonstrate the effectiveness of our proposed VAST-27M corpus and VAST foundation model. VAST achieves 22 new state-of-the-art results on various cross-modality benchmarks.

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ECAI Conference 2020 Conference Paper

Combination of Active Learning and Self-Paced Learning for Deep Answer Selection with Bayesian Neural Network

Qunbo Wang
Wenjun Wu 0001
Yuxing Qi
Zhimin Xin

Answer Selection is an important subtask of Question Answering tasks. For this learning-to-rank problem, deep learning methods have outperformed traditional methods. To train a high-quality deep answer selection model, it often requires large amounts of labeled data, which is a costly and noise-prone process. Active learning and semi-supervised learning are usually applied in the modelling training procedure to achieve optimal accuracy with fewer labeled training samples. However, traditional active learning methods rely on good uncertainty estimates that are hard to obtain with standard neural networks. And the performance of semi-supervised learning methods are always affected adversely by the quality of the pseudo-labeled data. In this work, we propose a new framework integrating active learning and self-paced learning in training deep answer selection models. This framework proposes an uncertainty quantification method based on Bayesian neural network, which can guide active learning and self-paced learning in the same iterative process of model training. Experiments were conducted on two kinds of deep answer selection models with real-world datasets including YahooCQA and SemiEvalCQA. The results reveal that the proposed method can significantly reduce the labeled samples for model training.

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