Qilong Han

AAAI Conference 2026 Conference Paper

Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning

• Yingnan Zhao
• Xinmiao Wang
• Dewei Wang
• Xinzhe Liu
• Dan Lu
• Qilong Han
• Peng Liu
• Chenjia Bai

Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the adaptive behavior controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains.

AAAI Conference 2025 Conference Paper

From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking

• Yuhan Zhao
• Rui Chen
• Li Chen
• Shuang Zhang
• Qilong Han
• Hongtao Song

Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models rely on pairwise loss functions to approximate full rankings, resulting in an immense performance gap. In this paper, we provide a novel analysis using the multiple ordinal classification concept to reveal the inevitable gap between a pairwise approximation and the ideal case. However, bridging the gap in practice encounters two formidable challenges: (1) none of the real-world datasets contains full ranking information; (2) there does not exist a loss function that is capable of consuming ranking information. To overcome these challenges, we propose a pseudo-ranking paradigm (PRP) that addresses the lack of ranking information by introducing pseudo-rankings supervised by an original noise injection mechanism. Additionally, we put forward a new ranking loss function designed to handle ranking information effectively. To ensure our method's robustness against potential inaccuracies in pseudo-rankings, we equip the ranking loss function with a gradient-based confidence mechanism to detect and mitigate abnormal gradients. Extensive experiments on four real-world datasets demonstrate that PRP significantly outperforms state-of-the-art methods.

AAMAS Conference 2025 Conference Paper

Multi-Ship Future Interaction Trajectory Prediction via Pre-Initializer Diffusion Model

• Kun Ma
• Qilong Han
• Jingzheng Yao

Real-time stochastic multi-ship trajectory modeling is crucial for maritime safety. However, it remains challenging due to the uncertainty of dynamic vessel intentions and their complex interactions. Most existing studies rely on deterministic social data from historical time steps for modeling, which often fail to capture the future states of interacting ships, leading to unrealistic trajectory overlaps. Recent research has demonstrated that diffusion models excel in trajectory prediction due to their high generation quality, training stability, and diversity. However, their slow sampling speed limits real-time perception in maritime environments, as generating high-quality trajectories typically requires hundreds of denoising steps. To address these challenges, we propose a Multi-Ship Future interaction trajectory prediction approach based on a Pre-initializer Diffusion model (MFPD). By training a parameterized pre-initializer to directly learn the joint distribution of multiple denoising steps in the reverse diffusion process, our method significantly reduces the time cost of denoising while retaining only a few steps for fine-tuning the distribution. Specifically, in addition to encoding historical trajectory information and social interactions as state embeddings, we also incorporate future trajectory and multimodal maritime environmental information as input condition embeddings to fully capture potential future interactions and environmental features. Experimental results demonstrate that the proposed model significantly improves performance on two real-world datasets while greatly accelerating the sampling speed, demonstrating the superiority in real-world maritime environments.

AAAI Conference 2024 Conference Paper

Adaptive Hardness Negative Sampling for Collaborative Filtering

• Riwei Lai
• Rui Chen
• Qilong Han
• Chi Zhang
• Li Chen

Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negative sampling methods that they can only select negative samples of a fixed hardness level, leading to the false positive problem (FPP) and false negative problem (FNP). We then propose a new paradigm called adaptive hardness negative sampling (AHNS) and discuss its three key criteria. By adaptively selecting negative samples with appropriate hardnesses during the training process, AHNS can well mitigate the impacts of FPP and FNP. Next, we present a concrete instantiation of AHNS called AHNS_{p<0}, and theoretically demonstrate that AHNS_{p<0} can fit the three criteria of AHNS well and achieve a larger lower bound of normalized discounted cumulative gain. Besides, we note that existing negative sampling methods can be regarded as more relaxed cases of AHNS. Finally, we conduct comprehensive experiments, and the results show that AHNS_{p<0} can consistently and substantially outperform several state-of-the-art competitors on multiple datasets.

IJCAI Conference 2021 Conference Paper

Fine-Grained Air Quality Inference via Multi-Channel Attention Model

• Qilong Han
• Dan Lu
• Rui Chen

In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.

TCS Journal 2019 Journal Article

DOAMI: A distributed on-line algorithm to minimize interference for routing in wireless sensor networks

• Kejia Zhang
• Qilong Han
• Zhipeng Cai
• Guisheng Yin
• Junyu Lin

JBHI Journal 2014 Journal Article

Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph

• Haiwei Pan
• Pengyuan Li
• Qing Li
• Qilong Han
• Xiaoning Feng
• Linlin Gao

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.