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Li Jiang

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21 papers

2 author rows

AAAI Conference 2026 Conference Paper

SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization

Zhixiong Zhao
Fangxin Liu
Junjie Wang
Chenyang Guan
Zongwu Wang
Li Jiang
Haibing Guan

The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deployment on end-user devices. In this paper, we revisit the challenge of extreme LLM compression---targeting ultra-low-bit quantization for both activations and weights---from a Fourier frequency domain perspective. We propose SpecQuant, a two-stage framework that tackles activation outliers and cross-channel variance. In the first stage, activation outliers are smoothed and transferred into the weight matrix to simplify downstream quantization. In the second stage, we apply channel-wise low-frequency Fourier truncation to suppress high-frequency components while preserving essential signal energy, improving quantization robustness. Our method builds on the principle that most of the weight energy is concentrated in low-frequency components, which can be retained with minimal impact on model accuracy. To enable runtime adaptability, we introduce a lightweight truncation module during inference that adjusts truncation thresholds based on channel characteristics. On LLaMA-3 8B, SpecQuant achieves 4-bit quantization for both weights and activations, narrowing the zero-shot accuracy gap to only 1.5% compared to full precision, while delivering 2× faster inference and 3× lower memory usage.

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EAAI Journal 2025 Journal Article

A multi-scale deep feature memory and recovery network for multi-sensor fault diagnosis in the channel missing scenario

Tianao Zhang
Li Jiang
Jie Liu
Xin Zhang
Qing Zhang

In the field of data-driven intelligent fault diagnosis, the monitoring data provided by single sensor are typically inadequate to reveal the complex states of large-scale equipment comprehensively. Intelligent fault diagnosis techniques based on the fusion of multi-sensor signals have achieved considerable success. Nevertheless, the primary multi-channel fault diagnosis methods based on multi-sensor signals have not yet effectively resolved the problem of sudden sensor failures. Given the challenges mentioned above, we introduce the channel missing scenario to emulate the situation where partial channels are suddenly missing during online inference. To alleviate the impact of missing channels, we propose a multi-scale deep feature memory and recovery network (MDFMR). The feature memory and channel recovery mechanisms of MDFMR can improve the robustness of the model under channel missing scenario. The experiments were conducted on two rotating machinery datasets. Experimental results demonstrated that in the most severe channel missing scenario, conventional multi-channel diagnosis methods become unreliable, while MDFMR maintains a diagnosis accuracy of over 95%.

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AAAI Conference 2025 Conference Paper

Are Expressive Models Truly Necessary for Offline RL?

Guan Wang
Haoyi Niu
Jianxiong Li
Li Jiang
Jianming Hu
Xianyuan Zhan

Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm. Sequential modeling, however, requires capturing accurate dynamics across long horizons in trajectory data to ensure reasonable policy performance. To meet this requirement, leveraging large, expressive models has become a popular choice in recent literature, which, however, comes at the cost of significantly increased computation and inference latency. Contradictory yet promising, we reveal that lightweight models as simple as shallow 2-layer MLPs, can also enjoy accurate dynamics consistency and significantly reduced sequential modeling errors against large expressive models by adopting a simple recursive planning scheme: recursively planning coarse-grained future sub-goals based on current and target information, and then executes the action with a goal-conditioned policy learned from data relabeled with these sub-goal ground truths. We term our method as Recursive Skip-Step Planning (RSP). Simple yet effective, RSP enjoys great efficiency improvements thanks to its lightweight structure, and substantially outperforms existing methods, reaching new SOTA performances on the D4RL benchmark, especially in multi-stage long-horizon tasks.

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JBHI Journal 2025 Journal Article

Enhancing Ultrasound Scanning Skills in a Leader–Follower Robotic System through Expert Hand Impedance Regulation

Baoshan Niu
Dapeng Yang
Le Zhang
Yiming Ji
Li Jiang
Hong Liu

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader–follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader–follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57. 9% and 73. 6%, the contact force variance decreased by 55. 2% and 42. 5%, and the US image confidence increased by 22. 0% and 23. 8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32. 0% and 25. 3%, respectively, and increases confidence by 7. 3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader–follower US scanning and rapidly improve surgical skills.

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EAAI Journal 2025 Journal Article

Inversion of tunnel fires using limited monitored temperature data based on transfer learning approach and full-scale scenario applications

Li Jiang
Xin Guo
Ying Yang
Dong Yang

Numerical simulation coupled with deep learning models has proven effective for inversing fire source parameters in tunnel fires. However, models trained exclusively on numerical simulation data often struggle to adapt to diverse tunnel scenarios due to variations in tunnel geometry, sensor placement, and fluctuating heat release rate (HRR). To address these challenges, this study proposes a novel transfer learning framework. The model is pre-trained on numerical simulation datasets and uses temperature data from a limited number of sensors beneath the tunnel ceiling to inverse fire location and real-time HRR. The results demonstrate that the model achieves high accuracy using only six temperature sensors, even when positioned far from the fire source, achieving Coefficient of determination (R2) values exceeding 0. 99. Full model fine-tuning enhances the model's adaptability to variations in tunnel geometry, demonstrating remarkable performance in reduced-scale fire tests with R2 values above 0. 99 for fire location and 0. 86 for HRR. The method is further validated in full-scale tunnel fires with highly variable HRR patterns. To handle sensor damage or data loss, the model utilizes temperature data from sensors farther from the fire source, maintaining R2 values above 0. 99 for fire location and 0. 82 for HRR in the test set. Additionally, the model performs well in inversing HRR during the growth and stable periods of fires in both reduced-scale and full-scale tunnels, achieving Mean Absolute Percentage Error (MAPE) values below 0. 2. This capability is critical for early fire detection and effective emergency response in real tunnel fire scenarios.

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AAAI Conference 2025 Conference Paper

LiON: Learning Point-Wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

Shaocong Xu
Pengfei Li
Qianpu Sun
Xinyu Liu
Yang Li
Shihui Guo
Zhen Wang
Bo Jiang

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying outlier points in a LiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich information. While former SOTA methods adopt heuristic architectures, we revisit this problem from the perspective of Selective Classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any inlier categories but learns a point-wise abstaining penalty with a margin-based loss. Apart from learning paradigms, synthesizing outliers to approximate unlimited real outliers is also critical, so we propose a strong synthesis pipeline that generates outliers originated from various factors: object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve SOTA results.

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YNIMG Journal 2025 Journal Article

Motor-cognitive aging: The role of motor cortex and its pathways

Jiaqi Wen
Zifei Liang
Chenyang Li
Huize Pang
Li Jiang
Jiayi Li
Xiaojun Guan
Jiangyang Zhang

BACKGROUND: Motor and cognitive decline are hallmark features of aging. In the primary motor cortex (M1), pyramidal neurons project to the corticospinal tract (CST), a well-established motor pathway, and send collaterals to the ipsilateral striatum, forming the corticostriatal tract (CStrT). While the CST has been extensively studied, the role of the CStrT in motor and cognitive aging remains poorly understood. METHODS: We analyzed T1- and T2-weighted MRI, multi-delay arterial spin labeling, and multi-shell diffusion MRI data from 339 right-handed healthy adults (aged 36-90 years) in the Human Connectome Project-Aging dataset. Age-related trajectories of M1 structure and hemodynamics, as well as CST and CStrT microstructure, were assessed. Segment-wise along-tract analyses were conducted to identify localized tract degeneration. Mediation analyses were performed to examine whether tract integrity linked M1 atrophy to motor and cognitive performance. RESULTS: With age, M1 exhibited reduced volume and hemodynamics, altered T1/T2 ratio, and increased cortical curvature, reflecting structural and hemodynamic alterations. Along-tract analyses revealed localized microstructural degeneration in the CST adjacent to M1, whereas the CStrT showed more extensive degeneration along its trajectory. These tract changes were associated with structural and hemodynamic alterations in M1. Furthermore, integrity of the dominant (left) CST and CStrT mediated the relationship between ipsilateral M1 atrophy and motor decline. Notably, CStrT integrity also mediated the association between M1 atrophy and motor cognition decline. CONCLUSION: These findings establish age-related structural and functional degeneration of M1 and its pathways, highlighting the CStrT as a critical mediator between motor cortical atrophy and both motor and cognitive decline. These normative imaging markers of healthy aging may help inform the early detection of neurodegenerative diseases.

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YNIMG Journal 2024 Journal Article

In vivo mapping of hippocampal venous vasculature and oxygenation using susceptibility imaging at 7T

Chenyang Li
Sagar Buch
Zhe Sun
Marco Muccio
Li Jiang
Yongsheng Chen
E. Mark Haacke
Jiangyang Zhang

Mapping the small venous vasculature of the hippocampus in vivo is crucial for understanding how functional changes of hippocampus evolve with age. Oxygen utilization in the hippocampus could serve as a sensitive biomarker for early degenerative changes, surpassing hippocampal tissue atrophy as the main source of information regarding tissue degeneration. Using an ultrahigh field (7T) susceptibility-weighted imaging (SWI) sequence, it is possible to capture oxygen-level dependent contrast of submillimeter-sized vessels. Moreover, the quantitative susceptibility mapping (QSM) results derived from SWI data allow for the simultaneous estimation of venous oxygenation levels, thereby enhancing the understanding of hippocampal function. In this study, we proposed two potential imaging markers in a cohort of 19 healthy volunteers aged between 20 and 74 years. These markers were: 1) hippocampal venous density on SWI images and 2) venous susceptibility ( Δ χ vein ) in the hippocampus-associated draining veins (the inferior ventricular veins (IVV) and the basal veins of Rosenthal (BVR) using QSM images). They were chosen specifically to help characterize the oxygen utilization of the human hippocampus and medial temporal lobe (MTL). As part of the analysis, we demonstrated the feasibility of measuring hippocampal venous density and Δ χ vein in the IVV and BVR at 7T with high spatial resolution (0. 25 × 0. 25 × 1 mm3). Our results demonstrated the in vivo reconstruction of the hippocampal venous system, providing initial evidence regarding the presence of the venous arch structure within the hippocampus. Furthermore, we evaluated the age effect of the two quantitative estimates and observed a significant increase in Δ χ vein for the IVV with age (p = 0. 006, r2 = 0. 369). This may suggest the potential application of Δ χ vein in IVV as a marker for assessing changes in atrophy-related hippocampal oxygen utilization in normal aging and neurodegenerative diseases such as AD and dementia.

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EAAI Journal 2023 Journal Article

A Deep Convolution Multi-Adversarial adaptation network with Correlation Alignment for fault diagnosis of rotating machinery under different working conditions

Li Jiang
Wei Lei
Shuaiyu Wang
Shunsheng Guo
Yibing Li

Domain adaptation (DA) approaches have been extensively applied to the diagnosis of rotating machinery faults under different working conditions. However, most DA-based methods perform poorly in practical situations since they generally only consider the global distribution or subdomain distribution of the source and target domains. Thus, we propose a novel Deep Convolution Multi-Adversarial adaptation network with Correlation Alignment (DCMACA). DCMACA consists of an improved deep convolutional feature extractor, a domain adaptation module, and a label classifier. The improved deep convolutional feature extractor comprises ordinary convolutional layers, depthwise convolution layers, Squeeze and Excitation modules, skip connection operations, an average pooling layer, and a fully connected layer. The domain adaptation module introduces multiple domain discriminators and Coral distance to align the subdomain distribution and global distribution of features extracted by the feature extractor, respectively. The softmax function is employed as the label classifier. Based on DCMACA, we presented a new approach for identifying faults in rotating machinery under different operating conditions. First, the original vibration signals are converted into the time-frequency maps of size 64 × 64 via the continuous wavelet transform and bilinear interpolation technologies. Subsequently, the time-frequency maps are input to DCMACA to complete the extraction of transferable features and fault identification. The proposed DCMACA fault identification approach was evaluated through two experiments, where it achieved an average accuracy of 98. 84% in 18 migration diagnostic tasks. The comprehensive results reveal that the presented approach can realize higher diagnostic accuracies, robustness, and superior generalization capability compared to the existing mainstream DA approaches.

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AAAI Conference 2023 Conference Paper

Learning Context-Aware Classifier for Semantic Segmentation

Zhuotao Tian
Jiequan Cui
Li Jiang
Xiaojuan Qi
Xin Lai
Yixin Chen
Shu Liu
Jiaya Jia

Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing. Different from the mainstream literature where the efficacy of strong backbones and effective decoder heads has been well studied, in this paper, additional contextual hints are instead exploited via learning a context-aware classifier whose content is data-conditioned, decently adapting to different latent distributions. Since only the classifier is dynamically altered, our method is model-agnostic and can be easily applied to generic segmentation models. Notably, with only negligible additional parameters and +2\% inference time, decent performance gain has been achieved on both small and large models with challenging benchmarks, manifesting substantial practical merits brought by our simple yet effective method. The implementation is available at https://github.com/tianzhuotao/CAC.

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NeurIPS Conference 2023 Conference Paper

Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

Peng Cheng
Xianyuan Zhan
Zhihao Wu
Wenjia Zhang
Youfang Lin
Shou cheng Song
Han Wang
Li Jiang

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability. Code is available at: https: //github. com/pcheng2/TSRL

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NeurIPS Conference 2022 Conference Paper

A Policy-Guided Imitation Approach for Offline Reinforcement Learning

Haoran Xu
Li Jiang
Li Jianxiong
Xianyuan Zhan

Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution generalization but suffer from erroneous off-policy evaluation. Imitation-based methods avoid off-policy evaluation but are too conservative to surpass the dataset. In this study, we propose an alternative approach, inheriting the training stability of imitation-style methods while still allowing logical out-of-distribution generalization. We decompose the conventional reward-maximizing policy in offline RL into a guide-policy and an execute-policy. During training, the guide-poicy and execute-policy are learned using only data from the dataset, in a supervised and decoupled manner. During evaluation, the guide-policy guides the execute-policy by telling where it should go so that the reward can be maximized, serving as the \textit{Prophet}. By doing so, our algorithm allows \textit{state-compositionality} from the dataset, rather than \textit{action-compositionality} conducted in prior imitation-style methods. We dumb this new approach Policy-guided Offline RL (\texttt{POR}). \texttt{POR} demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline RL. We also highlight the benefits of \texttt{POR} in terms of improving with supplementary suboptimal data and easily adapting to new tasks by only changing the guide-poicy.

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NeurIPS Conference 2022 Conference Paper

Motion Transformer with Global Intention Localization and Local Movement Refinement

Shaoshuai Shi
Li Jiang
Dengxin Dai
Bernt Schiele

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectories based on latent features or utilize dense goal candidates to identify agent's destinations, where the former strategy converges slowly since all motion modes are derived from the same feature while the latter strategy has efficiency issue since its performance highly relies on the density of goal candidates. In this paper, we propose the Motion TRansformer (MTR) framework that models motion prediction as the joint optimization of global intention localization and local movement refinement. Instead of using goal candidates, MTR incorporates spatial intention priors by adopting a small set of learnable motion query pairs. Each motion query pair takes charge of trajectory prediction and refinement for a specific motion mode, which stabilizes the training process and facilitates better multimodal predictions. Experiments show that MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges, ranking 1st on the leaderbaords of Waymo Open Motion Dataset. Code will be available at https: //github. com/sshaoshuai/MTR.

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NeurIPS Conference 2022 Conference Paper

Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

Xiaoyang Wu
Yixing Lao
Li Jiang
Xihui Liu
Hengshuang Zhao

As a pioneering work exploring transformer architecture for 3D point cloud understanding, Point Transformer achieves impressive results on multiple highly competitive benchmarks. In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work. In particular, we first propose group vector attention, which is more effective than the previous version of vector attention. Inheriting the advantages of both learnable weight encoding and multi-head attention, we present a highly effective implementation of grouped vector attention with a novel grouped weight encoding layer. We also strengthen the position information for attention by an additional position encoding multiplier. Furthermore, we design novel and lightweight partition-based pooling methods which enable better spatial alignment and more efficient sampling. Extensive experiments show that our model achieves better performance than its predecessor and achieves state-of-the-art on several challenging 3D point cloud understanding benchmarks, including 3D point cloud segmentation on ScanNet v2 and S3DIS and 3D point cloud classification on ModelNet40. Our code will be available at https: //github. com/Gofinge/PointTransformerV2.

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AAAI Conference 2022 Conference Paper

SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks

Fangxin Liu
Wenbo Zhao
Yongbiao Chen
Zongwu Wang
Li Jiang

Spiking Neural Networks (SNNs) have recently attracted enormous research interest since their event-driven and braininspired structure enables low-power computation. In image recognition tasks, the best results achieved by SNN so far utilize ANN-SNN conversion methods that replace activation functions in artificial neural networks (ANNs) with integrate-and-fire neurons. Compared to source ANNs, converted SNNs usually suffer from accuracy loss and require a considerable number of time steps to achieve competitive accuracy. We find that the performance degradation of converted SNN stems from the fact that the information capacity of spike trains in transferred networks is smaller than that of activation values in source ANN, resulting in less information being passed during SNN inference. To better correlate ANN and SNN for better performance, we propose a conversion framework to mitigate the gap between the activation value of source ANN and the generated spike train of target SNN. The conversion framework originates from exploring an identical relation in the conversion and exploits temporal separation scheme and novel neuron model for the relation to hold. We demonstrate almost lossless ANN-SNN conversion using SpikeConverter for a wide variety of networks on challenging datasets including CIFAR-10, CIFAR-100, and ImageNet. Our results also show that SpikeConverter achieves the abovementioned accuracy across different network architectures and datasets using 32X - 512X fewer inference time-steps than state-of-the-art ANN- SNN conversion methods.

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AAAI Conference 2021 Conference Paper

CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud

Wu Zheng
Weiliang Tang
Sijin Chen
Li Jiang
Chi-Wing Fu

Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align. To address this issue, we present a new single-stage detector named the Confident IoU-Aware Single-Stage object Detector (CIA-SSD). First, we design the lightweight Spatial-Semantic Feature Aggregation module to adaptively fuse high-level abstract semantic features and low-level spatial features for accurate predictions of bounding boxes and classification confidence. Also, the predicted confidence is further rectified with our designed IoU-aware confidence rectification module to make the confidence more consistent with the localization accuracy. Based on the rectified confidence, we further formulate the Distance-variant IoU-weighted NMS to obtain smoother regressions and avoid redundant predictions. We experiment CIA-SSD on 3D car detection in the KITTI test set and show that it attains top performance in terms of the official ranking metric (moderate AP 80. 28%) and above 32 FPS inference speed, outperforming all prior single-stage detectors. The code is available at https: //github. com/Vegeta2020/CIA-SSD.

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YNICL Journal 2018 Journal Article

Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status

Jing Zhao
Ji-bin Li
Jing-yan Wang
Yu-liang Wang
Da-wei Liu
Xin-bei Li
Yu-kun Song
Yi-su Tian

Background and purpose: ) mutation status. Methods: Forty-two patients (male: 23, female: 19, mean age: 44.5 y) were recruited and underwent whole brain NODDI examination. Intracellular volume fraction (icvf) and orientation dispersion index (ODI) maps were derived. Three ROIs were manually placed on TP and PT regions for each case. The corresponding average values of icvf and ODI were calculated, and their diagnostic efficiency was assessed. Results: mutation status. Conclusions: mutation status has not been fully explored, as a larger sample size may be necessary to uncover benefits.

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ICRA Conference 2011 Conference Paper

A novel 6-DoF biped active walking robot - Walking gaits, patterns and experiments

Yisheng Guan
Xuefeng Zhou
Haifei Zhu
Li Jiang
Chuanwu Cai
Xianmin Zhang
Hong Zhang 0013

Combining the advantages of active and passive walking robots, we have developed a novel active biped walking robot with only six DoFs. The robot is built with six 1-DoF joint modules and two wheels as the feet. It achieves locomotion in special gaits different from those of traditional biped robots. In this paper, this novel biped robot is introduced, and four walking gaits, namely turning-around gait, foot-wheel hybrid gait, side-stepping gait and turning-over gait are proposed, and their walking patterns and motion planning are presented and analyzed. Walking experiments are carried out to verify the locomotion function, the effectiveness of the presented gaits and to illustrate the features of this novel biped robot. It has been shown that biped active walking may be achieved with only a few DoFs and simple kinematic configuration.

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IROS Conference 2011 Conference Paper

Climbot: A modular bio-inspired biped climbing robot

Yisheng Guan
Li Jiang
Haifei Zhu
Xuefeng Zhou
Chuanwu Cai
Wenqiang Wu
Zhanchu Li
Hong Zhang 0013

High-rise tasks in agriculture, forestry and building industry requires robots possessing climbing function. Motivated by these potential applications and inspired by the climbing motion of animals such as inchworms, we have developed a novel biped climbing robot - Climbot. Built with a modular approach, the robot consists of five 1-DoF joint modules connected in series and two special grippers mounted at the ends. With this configuration, Climbot is able not only to climb a variety of media, but also to grasp and manipulate objects, and hence is a “mobile” manipulator. In this paper, we first introduce the development of this novel robot, and then illustrate three climbing gaits based on the unique configuration of the robot. Experiments of climbing poles are carried out to verify the climbing functions and to demonstrate potential application of the proposed robot.

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IROS Conference 2009 Conference Paper

Development of novel robots with modular methodology

Yisheng Guan
Li Jiang
Xianmin Zhang
Hong Zhang 0013
Xuefeng Zhou

Modules have been widely used in the development of re-configurable robots and snake-like robots. Modular methodology can also be applied in design of other robots. To build robots flexibly and quickly with low costs, we have developed two basic joint modules and several functional modules including grippers, suckers and wheels/feet as end-effectors. In this paper, we introduce the development of these modules, and present several novel robots built using them. Specifically, we show how to use them to set up a manipulator, a 6-DoF biped walking robot, a wheeled mobile robot, a biped tree-climbing robot, and a biped wall-climbing robot. It has been shown that a few modules can easily spawn a variety of novel robots with modular methodology.

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YNIMG Journal 2008 Journal Article

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

Susumu Mori
Kenichi Oishi
Hangyi Jiang
Li Jiang
Xin Li
Kazi Akhter
Kegang Hua
Andreia V. Faria

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

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