Author name cluster

Sheng Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

32 papers

2 author rows

AAAI Conference 2026 Conference Paper

GraphGrasp: Lightweight and Efficient Graph-Guided 6-DoF Robotic Grasp Pose Estimation Network

Sheng Yu
Di-Hua Zhai
Yuanqing Xia

6-DoF object grasping is a crucial skill for embodied intelligent robots. Previous methods often rely on large-scale networks for feature extraction, followed by grasp pose prediction, which increases the network's parameter count and overlooks the geometric and graph features of the point cloud. To address these challenges, we propose GraphGrasp, a graph-guided 6-DoF grasping pose prediction method. It performs graph analysis from the perspectives of scene, object, and grasping graphs. First, we introduce a graph feature embedding method based on local-global features to model the scene graph effectively. Then, we use a graph transformer strategy to represent spatial relationships between objects in the object graph. Finally, we propose a multi-metric, multi-level grasp pose evaluation algorithm to predict and explore graspable points, enabling effective construction of grasp graphs and accurate grasp pose evaluation. We test GraphGrasp on the GraspNet-1Billion dataset, and the results show that, compared to previous methods, it achieves nearly the same performance with about 1/5 of the parameters of state-of-the-art methods, significantly improving grasp pose prediction speed. Additionally, in real-world robot grasping scenarios, GraphGrasp outperforms previous methods in practical grasp pose prediction tasks.

PDF Details DOI

AAAI Conference 2026 Conference Paper

LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation

Hao Jiang
Guoquan Wang
Donglin Zhou
Sheng Yu
Yang Zeng
Wencong Zeng
Kun Gai
Guorui Zhou

Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-based methods typically encode item textual information via prompt design and generate discrete semantic IDs through item tokenization. However, in domain-specific tasks such as local-life services, simply injecting location information into prompts fails to capture fine-grained spatial characteristics and real-world distance awareness among items. To address this, we propose LGSID, an LLM-Aligned Geographic Item Tokenization Framework for Local-life Recommendation. This framework consists of two key components: (1) RL-based Geographic LLM Alignment, and (2) Hierarchical Geographic Item Tokenization. In the RL-based alignment module, we initially train a list-wise reward model to capture real-world spatial relationships among items. We then introduce a novel G-DPO algorithm that uses pre-trained reward model to inject generalized spatial knowledge and collaborative signals into LLMs while preserving their semantic understanding. Furthermore, we propose a hierarchical geographic item tokenization strategy, where primary tokens are derived from discrete spatial and content attributes, and residual tokens are refined using the aligned LLM’s geographic representation vectors. Extensive experiments on real-world Kuaishou industry datasets show that LGSID consistently outperforms state-of-the-art discriminative and generative recommendation models. Ablation studies, visualizations, and case studies further validate its effectiveness.

PDF Details DOI

AAAI Conference 2025 Conference Paper

KeyPose: Category-Level 6D Object Pose Estimation with Self-Adaptive Keypoints

Sheng Yu
Di-Hua Zhai
Yuanqing Xia

Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this paper, we first introduce a transformer-based keypoint prediction method for adaptive forecasting of point cloud keypoints. This method calculates the similarity between keypoint features and point cloud features, allowing keypoints to represent object geometry more effectively. Furthermore, to enhance the geometric feature construction of keypoints, we propose a graph-based keypoint feature aggregation method, which considers both the structural relationships between keypoints and the point cloud, strengthening the network's understanding of geometric structures. At this stage, keypoints remain at the geometric spatial level of the object and have not been predicted in NOCS. To improve the accuracy of keypoint prediction in NOCS, we design a NOCS voxelization method that divides NOCS into multiple voxels and accurately predicts NOCS keypoints within these voxels. Experimental results on multiple benchmark datasets demonstrate that our proposed KeyPose method outperforms all existing methods, achieving over 20% improvement in pose accuracy on some critical datasets.

PDF Details DOI

ICRA Conference 2025 Conference Paper

Rapid Dynamic Obstacle Avoidance for UAVs Enhanced by DVS and Neuromorphic Computing

Siyang Wang
Sheng Yu
Tingbang Liang
Yilin Shi
Yongqiang Ma
Pengju Ren

Achieving rapid and accurate dynamic obstacle avoidance is crucial for enhancing the survivability of unmanned aerial vehicles (UAVs) in hazardous conditions. To accomplish dynamic obstacle avoidance, sensors with high temporal resolution and efficient processing models are required. Dynamic vision sensors (DVS) fulfill the sensing requirements, while spiking neural networks (SNNs) address the processing demands. In this paper, we develop an end-to-end obstacle avoidance algorithm for UAVs using only a single monocular DVS as the sensor and further enhance accuracy and speed through our proposed mechanisms. The algorithm consists of three components: ego-motion compensation, an SNN model for movement analysis, and a force filter inspired by spiking neurons. In movement analysis, we propose the temporal potential pooling (TPP) and incremental event (EI) mechanisms to accelerate our SNN model. The real-flight experiments confirm that our algorithm achieves approximately 90% accuracy with a processing latency as low as 4ms on a GPU, surpassing state-of-the-art methods. Ablation studies show that the proposed method maintains high accuracy in movement detection while significantly reducing computational time. Our method operates in real-time, achieves high accuracy, and is feasible across a wide range of environments. Our code is available at https://github.com/AmperiaWang/oanet_s1 for reproducibility.

Details

AAAI Conference 2024 Conference Paper

CatFormer: Category-Level 6D Object Pose Estimation with Transformer

Sheng Yu
Di-Hua Zhai
Yuanqing Xia

Although there has been significant progress in category-level object pose estimation in recent years, there is still considerable room for improvement. In this paper, we propose a novel transformer-based category-level 6D pose estimation method called CatFormer to enhance the accuracy pose estimation. CatFormer comprises three main parts: a coarse deformation part, a fine deformation part, and a recurrent refinement part. In the coarse and fine deformation sections, we introduce a transformer-based deformation module that performs point cloud deformation and completion in the feature space. Additionally, after each deformation, we incorporate a transformer-based graph module to adjust fused features and establish geometric and topological relationships between points based on these features. Furthermore, we present an end-to-end recurrent refinement module that enables the prior point cloud to deform multiple times according to real scene features. We evaluate CatFormer's performance by training and testing it on CAMERA25 and REAL275 datasets. Experimental results demonstrate that CatFormer surpasses state-of-the-art methods. Moreover, we extend the usage of CatFormer to instance-level object pose estimation on the LINEMOD dataset, as well as object pose estimation in real-world scenarios. The experimental results validate the effectiveness and generalization capabilities of CatFormer. Our code and the supplemental materials are avaliable at https://github.com/BIT-robot-group/CatFormer.

PDF Details DOI

AIIM Journal 2024 Journal Article

Efficient symptom inquiring and diagnosis via adaptive alignment of reinforcement learning and classification

Hongyi Yuan
Sheng Yu

Medical automatic diagnosis aims to organize real-world diagnostic processes similar to those from human doctors and to achieve accurate diagnoses by interacting with patients. The task is formulated as a sequential decision-making problem with a series of information inquiry steps (asking about symptoms and ordering examinations) and the final diagnosis. Recent research has studied incorporating reinforcement learning for information inquiry and classification techniques for disease diagnosis, respectively. However, studies on efficiently and effectively combining the two procedures are still lacking. To address this issue, we devised an adaptive mechanism to align reinforcement learning and classification methods using distribution entropy as the medium. Additionally, we created a new dataset for patient simulation to address the lack of large-scale evaluation benchmarks. The dataset is extracted from the MedlinePlus knowledge base and contains significantly more diseases and more comprehensive symptom and examination information than existing datasets. Experimental evaluation shows that our method outperforms three current state-of-the-art methods on different datasets by achieving higher medical diagnostic accuracy with fewer inquiring turns.