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Hongwei Fan

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

A multi-source domain-invariant acoustic feature extraction network for rotating machinery fault diagnosis under unknown cross-working conditions

Xinyuan Zhang
Xiangang Cao
Hongwei Fan
Xin Yang
Yong Duan
Fuyuan Zhao
Xiangyu Li

In mechanical systems under cross-working conditions, acoustic feature extraction for rotating machinery faces numerous challenges, including difficulty in acquiring high-quality training data from multi-source domains and large divergence between inter-domain samples. Existing methods often suffer from insufficient constraints in the embedding space and limited model generalization. To address these issues, this paper proposes a Multi-source Domain-Invariant Acoustic Feature Extraction Network (DIAFENet) for unknown cross-working condition tasks. DIAFENet introduces a multi-granularity adversarial learning framework that jointly optimizes classification loss, domain adversarial loss, and a novel feature association-based class boundary constraint loss, aiming to learn discriminative and operation-condition-invariant acoustic representations. The core innovation lies in a three-level domain-invariant feature association strategy: (1) Global-level alignment minimizes overall domain divergence; (2) Subdomain-level alignment refines local feature distribution consistency; (3) Maximization of inter-class association distance within subdomains explicitly enlarges decision margins between classes. The framework integrates a spectrogram-based feature extractor with a self-attention pooling mechanism, and employs a Gradient Reversal Layer (GRL) to adversarially eliminate domain-specific information and promote domain-invariant representation learning. The effectiveness of DIAFENet is rigorously evaluated on 15 cross-operating-condition tasks across two public datasets and a Self-built dataset. The results showed that the average classification accuracy of DIAFENet was 98. 93 %, 97. 31 %, and 96. 55 %. The ablation experiment further verified that the proposed feature association constraint strategy improved accuracy by 2. 75 %, demonstrating its key role in enhancing the compactness and separability of the embedding space. This study provides a reliable acoustic feature extraction scheme for intelligent diagnosis of mechanical equipment under multi-source domain cross-working condition tasks.

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AAAI Conference 2026 Conference Paper

CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model

Zhuoyuan Yu
Yuxing Long
Zihan Yang
Chengyan Zeng
Hongwei Fan
Jiyao Zhang
Hao Dong

Existing vision-and-language navigation models often deviate from the correct trajectory when executing instructions. However, these models lack effective error correction capability, hindering their recovery from errors. To address this challenge, we propose Self-correction Flywheel, a novel post-training paradigm. Instead of considering the model’s error trajectories on the training set as a drawback, our paradigm emphasizes their significance as a valuable data source. We have developed a method to identify deviations in these error trajectories and devised innovative techniques to automatically generate self-correction data for perception and action. These self-correction data serve as fuel to power the model’s continued training. The brilliance of our paradigm is revealed when we re-evaluate the model on the training set, uncovering new error trajectories. At this time, the self-correction flywheel begins to spin. Through multiple flywheel iterations, we progressively enhance our monocular RGB-based VLA navigation model CorrectNav. Experiments on R2R-CE and RxR-CE benchmarks show CorrectNav achieves new state-of-the-art success rates of 65.1% and 69.3%, surpassing prior best VLA navigation models by 8.2% and 16.4%. Real robot tests in various indoor and outdoor environments demonstrate \method's superior capability of error correction, dynamic obstacle avoidance, and long instruction following.

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ICML Conference 2025 Conference Paper

BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly

Yan Shen 0035
Ruihai Wu
Yubin Ke
Xinyuan Song
Zeyi Li
Xiaoqi Li 0020
Hongwei Fan
Haoran Lu

Shape assembly, the process of combining parts into a complete whole, is a crucial skill for robots with broad real-world applications. Among the various assembly tasks, geometric assembly—where broken parts are reassembled into their original form (e. g. , reconstructing a shattered bowl)—is particularly challenging. This requires the robot to recognize geometric cues for grasping, assembly, and subsequent bimanual collaborative manipulation on varied fragments. In this paper, we exploit the geometric generalization of point-level affordance, learning affordance aware of bimanual collaboration in geometric assembly with long-horizon action sequences. To address the evaluation ambiguity caused by geometry diversity of broken parts, we introduce a real-world benchmark featuring geometric variety and global reproducibility. Extensive experiments demonstrate the superiority of our approach over both previous affordance-based and imitation-based methods.

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AAAI Conference 2025 Conference Paper

Interacted Object Grounding in Spatio-Temporal Human-Object Interactions

Xiaoyang Liu
Boran Wen
Xinpeng Liu
Zizheng Zhou
Hongwei Fan
Cewu Lu
Lizhuang Ma
Yulong Chen

Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined object classes. Therefore, we introduce a new open-world benchmark: Grounding Interacted Objects (GIO) including 1,098 interacted objects class and 290K interacted object boxes annotation. Accordingly, an object grounding task is proposed expecting vision systems to discover interacted objects. Even though today’s detectors and grounding methods have succeeded greatly, they perform unsatisfactorily in localizing diverse and rare objects in GIO. This profoundly reveals the limitations of current vision systems and poses a great challenge. Thus, we explore leveraging spatio-temporal cues to address object grounding and propose a 4D question-answering framework (4D-QA) to discover interacted objects from diverse videos. Our method demonstrates significant superiority in extensive experiments compared to current baselines.

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IROS Conference 2025 Conference Paper

SimLauncher: Launching Sample-Efficient Real-World Robotic Reinforcement Learning via Simulation Pre-Training

Mingdong Wu
Lehong Wu
Yizhuo Wu
Weiyao Huang
Hongwei Fan
Zheyuan Hu
Haoran Geng
Jinzhou Li

Autonomous learning of dexterous, long-horizon robotic skills has been a longstanding pursuit of embodied AI. Recent advances in robotic reinforcement learning (RL) have demonstrated remarkable performance and robustness in real-world visuomotor control tasks. However, applying RL in the real world faces challenges such as low sample efficiency, slow exploration, and significant reliance on human intervention. In contrast, simulators offer a safe and efficient environment for extensive exploration and data collection, while the visual sim-to-real gap, often a limiting factor, can be mitigated using real-to-sim techniques. Building on these, we propose SimLauncher, a novel framework that combines the strengths of real-world RL and real-to-sim-to-real approaches to overcome these challenges. Specifically, we first pre-train a visuomotor policy in the digital twin simulation environment, which then benefits real-world RL in two ways: (1) bootstrapping target values using extensive simulated demonstrations and real-world demonstrations derived from pre-trained policy rollouts, and (2) Incorporating action proposals from the pre-trained policy for better exploration. We conduct comprehensive experiments across multi-stage, contact-rich, and dexterous hand manipulation tasks. Compared to prior real-world RL approaches, SimLauncher significantly improves sample efficiency and achieves near-perfect success rates. We hope this work serves as a proof of concept and inspires further research on leveraging large-scale simulation pre-training to benefit real-world robotic RL.

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NeurIPS Conference 2023 Conference Paper

BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series

Andrea Nascetti
Ritu Yadav
Kirill Brodt
Qixun Qu
Hongwei Fan
Yuri Shendryk
Isha Shah
Christine Chung

Above Ground Biomass is an important variable as forests play a crucial role in mitigating climate change as they act as an efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven to provide reliable estimations of forest biomass. Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale. However, the full potential of dense multi-modal satellite time series data, in combination with modern deep learning approaches, has yet to be fully explored. The aim of the "BioMassters" data challenge and benchmark dataset is to investigate the potential of multi-modal satellite data (Sentinel-1 SAR and Sentinel-2 MSI) to estimate forest biomass at a large scale using the Finnish Forest Centre's open forest and nature airborne LiDAR data as a reference. The performance of the top three baseline models shows the potential of deep learning to produce accurate and higher-resolution biomass maps. Our benchmark dataset is publically available at https: //huggingface. co/datasets/nascetti-a/BioMassters (doi: 10. 57967/hf/1009) and the implementation of the top three winning models are available at https: //github. com/drivendataorg/the-biomassters.

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