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Zhiguo Lu

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation

Zhiguo Lu
Jianwen Lou
Mingjun Ma
Hairong Jin
Youyi Zheng
Kun Zhou

3D teeth segmentation, involving the localization of tooth instances and their semantic categorization in 3D dental models, is a critical yet challenging task in digital dentistry due to the complexity of real-world dentition. In this paper, we propose 3DTeethSAM, an adaptation of the Segment Anything Model 2 (SAM2) for 3D teeth segmentation. SAM2 is a pretrained foundation model for image and video segmentation, demonstrating a strong backbone in various downstream scenarios. To adapt SAM2 for 3D teeth data, we render images of 3D teeth models from predefined views, apply SAM2 for 2D segmentation, and reconstruct 3D results using 2D-3D projections. Since SAM2's performance depends on input prompts and its initial outputs often have deficiencies, and given its class-agnostic nature, we introduce three light-weight learnable modules: (1) a prompt embedding generator to derive prompt embeddings from image embeddings for accurate mask decoding, (2) a mask refiner to enhance SAM2's initial segmentation results, and (3) a mask classifier to categorize the generated masks. Additionally, we incorporate Deformable Global Attention Plugins (DGAP) into SAM2's image encoder. The DGAP enhances both the segmentation accuracy and the speed of the training process. Our method has been validated on the 3DTeethSeg benchmark, achieving an IoU of 91.90% on high-resolution 3D teeth meshes, establishing a new state-of-the-art in the field.

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EAAI Journal 2025 Journal Article

Gait pattern recognition based on electroencephalogram signals with common spatial pattern and graph attention networks

Yanzheng Lu
Hong Wang
Zhiguo Lu
Jianye Niu
Chong Liu

Assisting human locomotion in various gait patterns is one of the challenges in the interaction between human and lower limb exoskeleton. In this paper, we propose the graph attention network with electroencephalogram (EEG) signal graph structure data constructed by common spatial pattern (CSP) model to extract the spatial–temporal information from multi-channel EEG signals to recognize gait patterns including walk, run, stair descent, stair ascent, stand-to-sit, sit-to-stand, and jump. The CSP spatial filters are analyzed for EEG signals with multiple gait patterns. The EEG signal graph structure data is constructed based on the spatial–temporal features and spatial domain features of the CSP filtered data. The graph structure based on the correlation between channels and the graph structure based on the spatial relative position of EEG electrodes are constructed for comparison. The gait pattern recognition performance of the proposed model is significantly higher than that of comparison models, and the average recognition accuracy is 86. 747%. The accuracy of gait pattern recognition along gait cycle is analyzed. Based on the analysis of the EEG signal graph structure reconstructed by multi-head attention coefficients of the proposed model, the learning characteristics of the model can be obtained. The relationship between EEG channels is analyzed based on the graph structures the models focusing on. Finally, the proposed method is validated on the open access brain-computer interface (BCI) competition IV Datasets 2a of EEG signals.

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IROS Conference 2012 Conference Paper

Locomotion selection of Multi-Locomotion Robot based on Falling Risk and moving efficiency

Taisuke Kobayashi
Tadayoshi Aoyama
Kosuke Sekiyama
Zhiguo Lu
Yasuhisa Hasegawa
Toshio Fukuda

This paper deals with a method of locomotion selection based on Falling Risk and moving efficiency. The robot estimates information from sensors by solving state equation. The robot evaluates the Falling Risk as an indicator of uncertainty. Falling Risk is derived from measured information by using Bayesian Network. Locomotion selection during walking is modeled as a Semi-Markov Decision Process and the most appropriate locomotion is selected by using the greedy algorithm. As a result, the robot can move in the environment that is difficult to travel by single locomotion mode, maintaining the maximum moving efficiency.

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IROS Conference 2012 Conference Paper

Optimal control of energetically efficient ladder decent motion with internal stress adjustment using key joint method

Zhiguo Lu
Kosuke Sekiyama
Tadayoshi Aoyama
Yasuhisa Hasegawa
Taisuke Kobayashi
Toshio Fukuda

For multi-contact robot motion, a closed chain is formed by robot links and the environment. This paper proposes a new methodology named “key joint method” for reducing the energy cost by adjusting an internal stress inside a closed chain. Firstly, we analyze the internal stress theoretically taking the degrees of freedom (DOF) and the number of position actuated joints into consideration, then a practical key joint method is proposed by changing a suitable redundant position controlled joint to be force control. After that, a parametric family is introduced for representing various of possible motions subjected to the robot dynamics and other constraints. Finally, a general optimization method is proposed for planning an energetically efficient multi-contact robot motion taking the motion trajectories and internal stress into consideration. As an example, the pace gait ladder decent motion is taken to explain the principle and realization of the proposed method. As experimental evaluation shows, the key joint method is effective for reducing the energy cost in the multi-contact motion.

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IROS Conference 2011 Conference Paper

Shaping energetically efficient brachiation motion for a 24-DOF gorilla robot

Stepan S. Pchelkin
Anton S. Shiriaev
Uwe Mettin
Leonid B. Freidovich
Tadayoshi Aoyama
Zhiguo Lu
Toshio Fukuda

We consider a 24-degrees-of-freedom monkey robot that is supposed to perform brachiation locomotion, i. e. swinging from one row of a horizontal ladder to the next one using the arms. The robot hand is constructed as a planar hook so that the contact point about which the robot swings is a passive hinge. We identify the 10 most relevant degrees of freedom for this underactuated mechanical system and formulate a tractable search: (a) introduce a family of coordination patterns to be enforced on the dynamics with respect to a path coordinate; (b) formulate geometric equality constraints that are necessary for periodic locomotion; (c) generate trajectories from integrable reduced dynamics associated with the passive hinge; (d) evaluate the energetic cost of transport. Moreover, we observe that a linear approximation of the reduced dynamics can be used for trajectory generation which allows us to incorporate the gradient of the cost function into the search algorithm.

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IROS Conference 2010 Conference Paper

Walk-to-brachiate transfer of multi-locomotion robot with error recovery

Zhiguo Lu
Tadayoshi Aoyama
Kosuke Sekiyama
Yasuhisa Hasegawa
Toshio Fukuda

This paper describes walk-to-brachiate transfer of a multi-locomotion robot (MLR). The MLR has multiple types of locomotion such as biped walking, quadruped walking and brachiation. This transfer is carried out through vertical ladder climbing as the robot must raise its body to start brachiating. As a result we have designed two stable transfer motions from walk to climb and from climb to brachiate, while contact situations and constraints of the robot are changing during the transfers. In addition, we have proposed a control algorithm by considering the reaction force from environment, and the setting of parameter is based on a kinetic model of the robot in order to tolerate relative position errors between the robot and its environments such as rungs of the ladder. The robustness of the designed motions with error corrections is experimentally verified.

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