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Xiangbo Lin

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6 papers

1 author row

EAAI Journal 2026 Journal Article

A topology-aware segment anything model for domain-invariant crack segmentation

Xue Li
Siyi Yu
Shiyun Xiao
Xiangbo Lin
Zihan Zhao

Structural cracks play a vital role in prolonging the life of engineering structures and assessing safety risks. The precise segmentation and analysis of cracks has become challenging because of the complexity of background and diversity of morphology. In this study, we offer TopoSAM, a topology-aware model based on the Segment Anything Model (SAM) for domain-invariant crack segmentation. Particularly, we add a branch called Topology Serpentine Convolution Branch (TSCB) for recognizing the topological geometry and detailed features of the cracks in accordance with the characteristics of structural cracks. Cross Branch Fusor (CBF) is designed to fuse the features from TSCB and Image Encoder of SAM. Additionally, we present Background Adversarial Twin Learning(BATL) to remove the impact of background noise on the segmentation performance. The constructed twin samples with the same crack content but different backgrounds are sent into TopoSAM to participate in the training together, making the model actively ignore the background changes and concentrate on the structural cracks. TopoSAM is evaluated with seven publicly datasets. The experimental results demonstrate that TopoSAM outperforms the current state-of-the-art algorithms in generalization and has intense competition in crack segmentation accuracy.

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AAAI Conference 2026 Conference Paper

Dexterous Manipulation Transfer via Progressive Kinematic-Dynamic Alignment

Wenbin Bai
Qiyu Chen
Xiangbo Lin
Jw L
Quancheng Li
Hejiang Pan
Yi Sun

The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy learning. To address this challenge, we present a hand-agnostic manipulation transfer system. It efficiently converts human hand manipulation sequences from demonstration videos into high-quality dexterous manipulation trajectories without requirements of massive training data. To tackle the multi-dimensional disparities between human hands and dexterous hands, as well as the challenges posed by high-degree-of-freedom coordinated control of dexterous hands, we design a progressive transfer framework: first, we establish primary control signals for dexterous hands based on kinematic matching; subsequently, we train residual policies with action space rescaling and thumb-guided initialization to dynamically optimize contact interactions under unified rewards; finally, we compute wrist control trajectories with the objective of preserving operational semantics. Using only human hand manipulation videos, our system automatically configures system parameters for different tasks, balancing kinematic matching and dynamic optimization across dexterous hands, object categories, and tasks. Extensive experimental results demonstrate that our framework can automatically generate smooth and semantically correct dexterous hand manipulation that faithfully reproduces human intentions, achieving high efficiency and strong generalizability with an average transfer success rate of 73%, providing an easily implementable and scalable method for collecting robot dexterous manipulation data. Refer to the arXiv version for the appendix.

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EAAI Journal 2025 Journal Article

A single-demonstration guided manipulation learning with dexterous hand

Jianwen Li
Yinglan Lv
Xiangbo Lin
Jinglue Hang
Xuanheng Li
Yi Sun

Demonstration assisted reinforcement learning has been proven to be an extremely effective method for solving complex multi-fingered dexterous hand manipulation tasks. It usually requires costly and time-consuming expert demonstrations for each task, affecting the learning efficiency of the dexterous manipulation policy. To overcome this drawback, this paper devotes to use only one human demonstration per task to obtain a generalizable dexterous manipulation policy. And a novel ‘Basics-before-Extension’ policy learning strategy (BBE) is proposed for this purpose. It consists of two learning stages. In the ‘basics learning stage’, the dexterous hand extracts hand-object contact points as key clues from demonstration, facilitating a quick learning of the expert basic skill. While in ‘extension learning stage’, the designed joint policy training scheme enables the expert knowledge to be transferred and adapted to new environments, outputting generalizable policy. We present the distinctive overall framework from the low-cost demonstration data collection to the policy learning process. Meanwhile, BBE strategy has been experimentally validated on typical grasp and manipulation tasks, including relocating objects, opening door, hammering nails and functional grasp. The results indicate that the proposed BBE strategy can empower the multi-fingered dexterous hand with the intelligence of learning typical grasp and manipulation efficiently and accurately from a single demonstration.

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AAAI Conference 2025 Conference Paper

Multi-fingered Hand Grasps with Visuo-Tactile Fusion via Multi-Agent Deep Reinforcement Learning

Peida Jia
Xuanheng Li
Tianqiang Zhu
Rina Wu
Xiangbo Lin
Yi Sun

Humans achieve contact-rich dexterous grasping through the synergy of visual and tactile information. However, the high-dimensional action space of high DoF multi-fingered hands poses significant challenges to this operation. In this study, we address this complexity by controlling the robotic hand at the reduced dimensional level of individual fingers instead of the entire hand, and develop a finger-based multi-agent deep reinforcement learning strategy by regarding the wrist, arm, and each finger of the hand as intelligent agents. We commence by applying a single-agent reinforcement learning algorithm to guide the whole hand to reach the feasible approaching direction and distance to the object. Then, we develop neuroscience-inspired visuo-tactile fusion networks to train multiple agents to control their assigned fingers by effectively leveraging visual and tactile feedback. This enables dynamic and collaborative adjustments of finger-object interactions, ultimately achieving precise contact with specific areas of the objects. The grasping results on 8 objects show that our approach can achieve stable and compliant grasps. To the best of our knowledge, this is the first work that employs a finger-based multi-agent reinforcement learning approach to control the dexterous grasping process under the guidance of both visual and tactile feedback.

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EAAI Journal 2024 Journal Article

Deocclusion and integration of advantages for a better hand pose

Xuefeng Li
Xiangbo Lin

Estimating hand pose in the case of hand and object interaction faces the challenge of occlusion. Traditional methods that use contact information to alleviate this problem have limited applications because accurately estimating the required shape and pose of unfamiliar objects is a difficult task. This paper address this problem with occlusion removal at image level, removing the object by the proposed self-supervision method, which reduces the labor required to collect the paired labels of occlusion and deocclusion of the hand. In addition, this paper consider occlusion as valuable information and propose an integration strategy to enrich the extracted features from the occluded hand image and deoccluded hand image. Validation experiments are performed to show the proposed model’s main contributions. The experimental results on two widely-used public datasets demonstrate that the proposed model outperforms other state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

DexFuncGrasp: A Robotic Dexterous Functional Grasp Dataset Constructed from a Cost-Effective Real-Simulation Annotation System

Jinglue Hang
Xiangbo Lin
Tianqiang Zhu
Xuanheng Li
Rina Wu
Xiaohong Ma
Yi Sun

Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous hands to complete human-like functional grasp, such as grasp the handle of a cup or pressing the button of a flashlight, so as to enable robots to complete subsequent functional manipulation action autonomously, and there is no dataset with functional grasp pose annotations at present. This paper develops a unique Cost-Effective Real-Simulation Annotation System by leveraging natural hand's actions. The system is able to capture a functional grasp of a dexterous hand in a simulated environment assisted by human demonstration in real world. By using this system, dexterous grasp data can be collected efficiently as well as cost-effective. Finally, we construct the first dexterous functional grasp dataset with rich pose annotations. A Functional Grasp Synthesis Model is also provided to validate the effectiveness of the proposed system and dataset. Our project page is: https://hjlllll.github.io/DFG/.

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