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学者姓名：左国玉
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Abstract ：
Gradual correction with parallel external fixators (PEFs) is a common treatment strategy for foot-ankle deformities. Designing appropriate PEFs and developing correction-assisted software (CAS) with comprehensive functions are clinically important to assure a successful treatment and the proper recovery of the patient. However, existing PEFs are inadequately targeted for specific deformity types of foot-ankle, and CAS is not available for detecting multiple interference phenomena, thus, the safety and accuracy of the corrective process cannot be guaranteed. In this article, an electromechanical drive PEF prototype with good deformity-targeting property is proposed for a common type of foot-ankle deformity with five corrective degree-of-freedom. The system is supported with the CAS that involves digital reconstruction technology, trajectory planning technology, and safety inspection technology. In safety inspection technology, collision inspection is carried out based on the oriented bounding box method, and inspection of the distraction rod overstroke and of the singularity are realized based on the kinematic model. In this way, an interference-free correction scheme can be generated and executed using the proposed PEF system. Case simulation demonstrates the feasibility of correction-assisted technologies, the applicability of the prototype, and the postural synergy between the virtual model and substantiation.
Keyword ：
Assembly Assembly Medical services Medical services Correction-assisted software (CAS) Correction-assisted software (CAS) external fixator external fixator Safety Safety mechatronic orthopedic system mechatronic orthopedic system foot-ankle deformity correction foot-ankle deformity correction Inspection Inspection Mechatronics Mechatronics safety inspection technology safety inspection technology Visualization Visualization Trajectory Trajectory
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| GB/T 7714 | Ju, Jie , Dong, Mingjie , Zuo, Guoyu et al. Mechatronic 5-DOF Parallel External Fixator With Correction-Assisted Software for Correcting Foot-Ankle Deformities [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2024 . |
| MLA | Ju, Jie et al. "Mechatronic 5-DOF Parallel External Fixator With Correction-Assisted Software for Correcting Foot-Ankle Deformities" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS (2024) . |
| APA | Ju, Jie , Dong, Mingjie , Zuo, Guoyu , Li, Jianfeng , Zuo, Shiping . Mechatronic 5-DOF Parallel External Fixator With Correction-Assisted Software for Correcting Foot-Ankle Deformities . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2024 . |
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Abstract ：
Learning from visual observations is a significant yet challenging problem in Reinforcement Learning (RL). Two respective problems, representation learning and task learning, need to solve to infer an optimal policy. Some methods have been proposed to utilize data augmentation in reinforcement learning to directly learn from images. Although these methods can improve generation in RL, they are often found to make the task learning unsteady and can even lead to divergence. We investigate the causes of instability and find it is usually rooted in high-variance of Q-functions. In this paper, we propose an easy-to-implement and unified method to solve above-mentioned problems, Data-augmented Reinforcement Learning with Ensemble Exploration and Exploitation (DAR-EEE). Bootstrap ensembles are incorporated into data augmented reinforcement learning and provide uncertainty estimation of both original and augmented states, which can be utilized to stabilize and accelerate the task learning. Specially, a novel strategy called uncertainty-weighted exploitation is designed for rational utilization of transition tuples. Moreover, an efficient exploration method using the highest upper confidence is used to balance exploration and exploitation. Our experimental evaluation demonstrates the improved sample efficiency and final performance of our method on a range of difficult image-based control tasks. Especially, our method has achieved the new state-of-the-art performance on Reacher-easy and Cheetah-run tasks.
Keyword ：
Bootstrap ensembles Bootstrap ensembles Reinforcement learning from images Reinforcement learning from images Robot learning Robot learning Data augmentation Data augmentation
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| GB/T 7714 | Zuo, Guoyu , Tian, Zhipeng , Huang, Gao . A stable data-augmented reinforcement learning method with ensemble exploration and exploitation [J]. | APPLIED INTELLIGENCE , 2023 , 53 (21) : 24792-24803 . |
| MLA | Zuo, Guoyu et al. "A stable data-augmented reinforcement learning method with ensemble exploration and exploitation" . | APPLIED INTELLIGENCE 53 . 21 (2023) : 24792-24803 . |
| APA | Zuo, Guoyu , Tian, Zhipeng , Huang, Gao . A stable data-augmented reinforcement learning method with ensemble exploration and exploitation . | APPLIED INTELLIGENCE , 2023 , 53 (21) , 24792-24803 . |
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Abstract ：
The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in clutter. To effectively perform grasping and pushing manipulations, robots need to perceive the position information of objects, including the coordinates and spatial relationship between objects (e.g., proximity, adjacency). The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in clutter. Specifically, a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping operations. In addition, the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial relationships between objects in cluttered environments. To further enhance the perception capacity of position information of the objects, the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function. A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency, task completion rate, grasping success rate and action efficiency compared to state-of-the-art end-to-end methods. Noted that the authors' system can be robustly applied to real-world use and extended to novel objects. Supplementary material is available at .
Keyword ：
deep neural networks deep neural networks deep learning deep learning intelligent robots intelligent robots
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| GB/T 7714 | Zhao, Min , Zuo, Guoyu , Yu, Shuangyue et al. Position-aware pushing and grasping synergy with deep reinforcement learning in clutter [J]. | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2023 , 9 (3) : 738-755 . |
| MLA | Zhao, Min et al. "Position-aware pushing and grasping synergy with deep reinforcement learning in clutter" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 9 . 3 (2023) : 738-755 . |
| APA | Zhao, Min , Zuo, Guoyu , Yu, Shuangyue , Gong, Daoxiong , Wang, Zihao , Sie, Ouattara . Position-aware pushing and grasping synergy with deep reinforcement learning in clutter . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2023 , 9 (3) , 738-755 . |
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Abstract ：
Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual arm coordination algorithm to improve the efficiency of coordination by considering both robot's actions and operating sequences of the tasks that need to use two arms to complete complex operations. Teleoperation demonstration is first performed to obtain the robot's human-like motion trajectories, so as to reduce the probability of the collisions between the two arms. The coordination diagram in time domain is then designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. A Coordination Pair Generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories and the action coordination. The effectiveness and efficiency of our method are verified on the simulation and physical experiments which execute the drug sorting task in nursing homes, respectively, on the ABB YuMi robot model and self-developed robot system. According to the experiment results, the operation time has been reduced by 9% and the collision area has been reduced by 7.5%.
Keyword ：
Dual arm coordination Dual arm coordination teleoperation demonstration teleoperation demonstration coordination diagram coordination diagram
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| GB/T 7714 | Zuo, Guoyu , Xu, Zichen , Huang, Gao . Dual Arm Coordination with Coordination Diagram Based on Teleoperation Demonstration [J]. | INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS , 2022 , 19 (04) . |
| MLA | Zuo, Guoyu et al. "Dual Arm Coordination with Coordination Diagram Based on Teleoperation Demonstration" . | INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 19 . 04 (2022) . |
| APA | Zuo, Guoyu , Xu, Zichen , Huang, Gao . Dual Arm Coordination with Coordination Diagram Based on Teleoperation Demonstration . | INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS , 2022 , 19 (04) . |
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Abstract ：
Grasping in cluttered scenes is an important issue in robotic manipulation. The cooperation of grasping and pushing actions based on reinforcement learning is an effective means to obtain the target object when it is completely blocked or there is no suitable grasping position around it. When exploring invisible objects, many existing methods depend excessively on model design and redundant grasping actions. We propose a graph-based deep reinforcement learning model to efficiently explore invisible objects and improve the performance for cooperative grasping and pushing tasks. Our model first extracts the state features and then estimates the Q value with different graph Q-Nets according to whether the target object is found. The graph-based Q-learning model contains an encoder, a graph reasoning module and a decoder. The encoder is used to integrate the state features such that the features of one region include those of other regions. The graph reasoning module captures the internal relationships of features between different regions through graph convolution networks. The decoder maps the features transformed by reasoning to the original state features. Our method achieves a 100% success rate in the task of exploring the target object and a success rate of more than 90% in the task of grasping and pushing cooperatively in simulation experiment, which performs better than many existing state-of-the-art methods. Our method is an effective means to help robots obtain completely occluded objects by grasping and pushing cooperation in the cluttered scenes. The verification experiment on the real robot further shows the generalization and practicability of our proposed model.
Keyword ：
Reinforcement learning Reinforcement learning Graph convolution network Graph convolution network Robotic manipulation Robotic manipulation Fully occluded object Fully occluded object Grasping and pushing Grasping and pushing
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| GB/T 7714 | Zuo, Guoyu , Tong, Jiayuan , Wang, Zihao et al. A Graph-Based Deep Reinforcement Learning Approach to Grasping Fully Occluded Objects [J]. | COGNITIVE COMPUTATION , 2022 , 15 (1) : 36-49 . |
| MLA | Zuo, Guoyu et al. "A Graph-Based Deep Reinforcement Learning Approach to Grasping Fully Occluded Objects" . | COGNITIVE COMPUTATION 15 . 1 (2022) : 36-49 . |
| APA | Zuo, Guoyu , Tong, Jiayuan , Wang, Zihao , Gong, Daoxiong . A Graph-Based Deep Reinforcement Learning Approach to Grasping Fully Occluded Objects . | COGNITIVE COMPUTATION , 2022 , 15 (1) , 36-49 . |
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Abstract ：
The ability of robots to perceive position information of objects is important for pushing and grasping tasks in clutter. We propose a Coordinate Attention Grasping and Pushing Network (Ca-GPNet) to learn synergistic grasping and pushing strategies in cluttered environments. We use both fully convolutional networks to predict actions: One predicts the grasping direction and position of the gripper, and the other predicts the initial push position and direction of the gripper. We propose to use the coordinate attention module to capture position information of objects along the horizontal and vertical directions of space and aggregate the features. The attention module extracts long-range interdependencies in one dimension while keeping the information about the location of objects in the another dimension. Then a pair of attention maps that perceive the location and orientation features of objects are generated to reinforce the perception of objects' locations by the network. The grasping success rate of the system is 73.7% and 71.4% of the action efficiency in the simulation. Our approach can be applied to real-world pushing and grasping tasks. Ca-GPNet grasping success rate is 79.2% and action efficiency is 75.3 % in the real world. Compared with baseline methods, our system can rapidly learn the pushing and grasping cooperative policy in a cluttered environment, with higher sample efficiency, grasping success rate and action efficiency.
Keyword ：
coordinate attention coordinate attention clutter clutter robots robots
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| GB/T 7714 | Zhao, Min , Zuo, Guoyu , Huang, Gao . Collaborative Learning of Deep Reinforcement Pushing and Grasping based on Coordinate Attention in Clutter [J]. | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI , 2022 : 156-161 . |
| MLA | Zhao, Min et al. "Collaborative Learning of Deep Reinforcement Pushing and Grasping based on Coordinate Attention in Clutter" . | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI (2022) : 156-161 . |
| APA | Zhao, Min , Zuo, Guoyu , Huang, Gao . Collaborative Learning of Deep Reinforcement Pushing and Grasping based on Coordinate Attention in Clutter . | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI , 2022 , 156-161 . |
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Abstract ：
机器人模仿的学习方法在行为运动的模仿上受到示教速度的限制,导致机器人模仿的速度也受到限制,无法更好发挥机器人的性能.为了提高机器人行为模仿的快速性,提出了一种自适应改变机器人模仿学习运动速度的方法.首先通过基于动态系统的方法建模示教运动,并将动态系统稳定的充分条件作为约束,确保行为模仿的稳定性.其次构造了一个随机器人状态到目标点的距离而变化的非线性函数,将非线性函数作为参数与系统模型结合,以便自适应地调整模仿的速度.最后给出了4种模仿学习评价的方法来评价模仿的性能.实验结果表明,提出的方法在保证机器人运动模仿的稳定性前提下很好地提高了行为模仿的速度.
Keyword ：
机器人 机器人 动态系统 动态系统 性能评价 性能评价 模仿学习 模仿学习 非线性函数 非线性函数
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| GB/T 7714 | 于建均 , 姚红柯 , 左国玉 et al. 自适应轨迹任务模仿的模仿学习方法研究 [J]. | 控制工程 , 2021 , 28 (2) : 266-274 . |
| MLA | 于建均 et al. "自适应轨迹任务模仿的模仿学习方法研究" . | 控制工程 28 . 2 (2021) : 266-274 . |
| APA | 于建均 , 姚红柯 , 左国玉 , 阮晓钢 . 自适应轨迹任务模仿的模仿学习方法研究 . | 控制工程 , 2021 , 28 (2) , 266-274 . |
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Abstract ：
分析传统工科教学存在的问题,提出多角度融合式课程教学改革,给出教学改革框架,并以微机原理与应用为例,从教学软件、教学硬件、实验环节和考核环节详细介绍课程改革措施,最后以课程改革后学生取得的创新成果说明教学改革框架的有效性.
Keyword ：
高等教育 高等教育 课程改革 课程改革 微机原理与应用 微机原理与应用 新工科 新工科 创新人才培养 创新人才培养
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| GB/T 7714 | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下面向创新能力培养的微机原理与应用课程改革 [J]. | 计算机教育 , 2021 , (2) : 108-112 . |
| MLA | 左国玉 et al. "新工科背景下面向创新能力培养的微机原理与应用课程改革" . | 计算机教育 2 (2021) : 108-112 . |
| APA | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下面向创新能力培养的微机原理与应用课程改革 . | 计算机教育 , 2021 , (2) , 108-112 . |
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新工科建设旨在培养多样化、创新型的卓越工程科技人才,高校作为人才培养的主要基地依然面临的问题是多数学生动手能力差,无法解决实际问题。机器人科技竞赛因其具有前沿性、综合性、实践性等特点成为了培养创新型人才的主要手段和重要突破口。为了加强创新人才的培养,文章依据新工科的内涵与特征,构建了以机器人科技竞赛为依托,教师为引导、学生为主体,重视跨教学培养,双向交叉的知识发展、宽口径知识发展和可持续发展方向的培养模式。通过教学实践表明,该培养模式在提高学生综合学科知识能力的同时,也培养了学生的创新思维和创新实践能力,提升了新工科背景下创新型科技人才的培养能力。
Keyword ：
创新人才 创新人才 培养模式 培养模式 新工科 新工科 机器人竞赛 机器人竞赛
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| GB/T 7714 | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下基于机器人竞赛的创新人才培养模式 [J]. | 高教学刊 , 2021 , PageCount-页数: 4 (06) : 44-47 . |
| MLA | 左国玉 et al. "新工科背景下基于机器人竞赛的创新人才培养模式" . | 高教学刊 PageCount-页数: 4 . 06 (2021) : 44-47 . |
| APA | 左国玉 , 雷飞 , 乔俊飞 . 新工科背景下基于机器人竞赛的创新人才培养模式 . | 高教学刊 , 2021 , PageCount-页数: 4 (06) , 44-47 . |
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Abstract ：
针对单目直接法依靠图像梯度进行优化容易陷入局部最优、且难以构建低纹理区域地图的问题,构建高精度IMU预积分模型,将惯性信息融合到图像跟踪过程中,为视觉跟踪提供精确的帧间运动约束及良好的初始化梯度方向信息,构建视觉惯性跟踪模型,提高了单目视觉的定位精度并实现半稠密地图构建;通过超像素图像分割提取出二维图像不同的轮廓位置,提出双重投影匹配算法确定出可靠的超像素与对应的3D空间点,通过RANSAC对低梯度图像区域进行平面拟合以及异常点剔除,完成低纹理区域的地图扩建,实现稠密点云地图的构建。实验结果表明,与传统视觉定位模型相比,直接法与惯性信息融合提高了系统的定位精度,在无GPU加速的情况下能构建精确...
Keyword ：
IMU预积分 IMU预积分 稠密点云地图 稠密点云地图 单目视觉 单目视觉 直接法 直接法
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| GB/T 7714 | 于建均 , 王洋 , 左国玉 et al. 融合惯性信息的单目直接法定位与稠密地图构建 [J]. | 控制工程 , 2021 , 28 (10) : 1967-1976 . |
| MLA | 于建均 et al. "融合惯性信息的单目直接法定位与稠密地图构建" . | 控制工程 28 . 10 (2021) : 1967-1976 . |
| APA | 于建均 , 王洋 , 左国玉 , 阮晓钢 , 李晨 . 融合惯性信息的单目直接法定位与稠密地图构建 . | 控制工程 , 2021 , 28 (10) , 1967-1976 . |
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