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Humanoid robot RHP Friends: Seamless Combination of Autonomous and Teleoperated Tasks in a Nursing Context

IEEE robotICS & AUTOMATION MAGAZINE 2025年第1期32卷 79-90页

作者： Benallegue, Mehdi Lorthioir, Guillaume Dallard, Antonin Cisneros-Limon, Rafael Kumagai, Iori Morisawa, Mitsuharu Kaminaga, Hiroshi Murooka, Masaki Andre, Antoine Gergondet, Pierre Kaneko, Kenji Caron, Guillaume Kanehiro, Fumio Kheddar, Abderrahmane Yukizaki, Soh Karasuyama, Junichi Murakami, Junichi Kamon, Masayuki CNRS AIST Joint Robot Lab Tsukuba 3058560 Japan Univ Montpellier Lab Informat Robot & Microelect Montpellier F-34090 Montpellier France Univ Montpellier Lab Informat Robot & Microelect Montpellier F-34090 Montpellier France Kawasaki Heavy Ind Co Ltd Tokyo 1058315 Japan

This article describes RHP Friends, a social humanoid robot developed to enable assistive robotic deployments in human-coexisting environments. As a use case application, we present its potential use in nursing by extending its capabilities to operate devices and tools according to the task and by enabling remote assistance operations. To meet a wide variety of tasks and situations in environments designed by and for humans, we develop a system that seamlessly integrates the slim and lightweight robot and several technologies: locomanipulation, multicontact motion, teleoperation, and object detection and tracking. We demonstrate the system's usage in a nursing application. The robot efficiently performs the daily task of patient transfer and a nonroutine task, represented by a request to operate a circuit breaker. This demonstration, held at the 2023 International robot Exhibition (IREX), was conducted three times a day over three days.

关键词： robots Humanoid robots Motors Medical services Sensors robot vision systems Cameras Automation Wheelchairs Tracking

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Design and Evaluation of a Lightweight, Ligaments-Inspired Knee Exoskeleton for Walking Assistance

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IEEE robotICS AND AUTOMATION LETTERS 2024年第10期9卷 8491-8498页

作者： Liu, Zhaoyuan Han, Jiaqi Han, Jianda Zhang, Juanjuan Nankai Univ Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

With proper assistance, knee exoskeletons can benefit humans with impaired leg function. Prior studies found that misalignment between the knee and the exoskeleton may cause harm and undermine assistance performance. Self-aligning mechanisms can reduce misalignment but implementing them with a simple and lightweight design remains a challenge. In this letter, we designed a lightweight (740 g) knee exoskeleton that can provide assistive torque for knee extension and flexion during walking. A compact elastic limiter was proposed to reduce misalignment using an integrated shock-absorbing slider. Drawing inspiration from the cruciate ligaments, springs were equipped to reduce the nonlinearities in the alignment process. With selected spring stiffness, preliminary pressure measurements between the frames and the human body showed the exoskeleton can reduce undesired interaction forces by 50.9% during walking assistance. Preliminary experimental results demonstrated the exoskeleton can achieve high torque control performance and reduce quadriceps activities during multiple gaits. For example, the activities of the rectus femoris, vastus medialis, and vastus lateralis were reduced by an average of 23.8%, 44.0%, and 34.0%, respectively during 8(degrees) uphill walking. The initial findings indicated the system's ability to provide effective gait assistance and made it promising for improving patients' mobility.

关键词： Exoskeletons Knee Torque Legged locomotion Thigh Springs Cables Prosthetics and exoskeletons physical human-robot interaction wearable robotics

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Design and Evaluation of a Bilateral Mobile Ankle Exoskeleton With High-Efficiency Actuation

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IEEE robotICS AND AUTOMATION LETTERS 2024年第6期9卷 5528-5535页

作者： Chen, Jianyu Ding, Jianquan Han, Jianda Zhang, Juanjuan Nankai Univ Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

Lower-limb exoskeletons can improve human mobility and endurance, especially for people with leg impairments. To minimize metabolic penalty and maximize assistance capacity, the design of the mobile exoskeleton needs to compromise between the system weight and actuation power. In the paper, we developed a lightweight high-torque mobile bilateral ankle exoskeleton with compliant end-effectors and a high-efficiency actuation. The system can provide 80 Nm assistive torques for both ankles with 3.4 kg entire exoskeleton weight by actuators' parallel connection and cooperative operation. Developed embedded hardware implemented a real-time torque controller comprising the damping-injected feedback control, model-based feedforward compensation, and iterative learning, achieving more than 10 Hz gain-limited closed-loop bandwidths and less than 2% torque tracking error. Preliminary physiological experiments demonstrated the exoskeleton's ability to reduce soleus muscle activities by over 50% at different walking speeds. Our work proved that the lightweight exoskeleton system can provide rather large torque assistance with fairly good bandwidth and tracking accuracy, promising to promote more exoskeleton applications in real life and rehabilitation.

关键词： Wearable robotics prosthetics and exoskeletons rehabilitation robotics

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On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data

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THEORETICAL POPULATION BIOLOGY 2024年 158卷 139-149页

作者： Wirtz, Johannes Guindon, Stephane CNRS UMR 5506 Lab Informat Robot & Microelect Montpellier Montpellier France

The introduction of the spatial Lambda-Fleming-Viot model (AV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine V & eacute;ber about ten years ago (Barton et al., 2010;Barton et al., 2013). The AV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus"issue with Wright and Mal & eacute;cot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the AV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the AV whereby lineages move rapidly over small distances. Using simulations, we show that the induced AV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lines of descent along birth-death trees do not generally provide a good approximation of the AV due to habitat boundaries effects that play an increasingly important role in the long run. Accounting for habitat boundaries through reflected Brownian motions considerably increases the similarity to the AV model however. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the AV model.

关键词： Spatial Lambda-Fleming-Viot Birth-death processes Simulations Dispersal Phylogeography

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MPOC-SLAM: An RGB-D SLAM System With Motion Probability and Object Category in High Dynamic Environments

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年第2期30卷 1061-1071页

作者： Wu, Songyang Zhang, Xuebo Zhang, Shiyong Song, Zhixing Wang, Runhua Yuan, Jing Nankai Univ Inst PLXSPACE Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

The performance of simultaneous localization and mapping (SLAM) is significantly impacted by dynamic objects. Existing dynamic SLAM methods still suffer from some major issues, such as insufficient use of semantic information, losing track when dynamic objects occupy the field of view, and inability to achieve real-time processing. In this article, we present SLAM with motion probability and object category (MPOC-SLAM), an RGB-D SLAM system capable of high-performance, real-time work in high dynamic environments. Considering intensive semantic information, our method introduces the concepts of motion probability (MP) and object category (OC), and tightly integrates them within the whole SLAM system. First, a priority feature extraction strategy is proposed based on MP, and then a dual-constraint feature matching method is designed with the adaptive probability threshold and category consistency. Both methods reduce the influence of dynamic objects in the extraction and association process. Second, to speed up the process of calculating reprojection errors, a semantic-guided geometric method with the partitioning hypothetico-deductive method is presented. The semantic-guided geometric method and instance segmentation are then fused through the variance weighted fusion with their confidences. Finally, as a more important part of SLAM, dynamic-aware weights are added to the original pose optimization, which assigns larger weights to static feature points and map points to reduce their alteration during optimization. We perform evaluations on the TUM public dataset and real-world environments. Compared with ORB-SLAM2, the percentage reduction of root mean square error of absolute trajectory error is as high as 98.72%. Results show that MPOC-SLAM realizes a significant improvement in accuracy as well as a more robust and real-time performance compared with other state-of-the-art dynamic SLAM methods.

关键词： Dynamic environments instance segmentation motion probability (MP) object category (OC) visual simultaneous localization and mapping (SLAM)

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Deep-Learning-Based Automated Morphology Analysis With Atomic Force Microscopy

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 7662-7673页

作者： Chang, Yingao Liu, Zhiang Wu, Yinan Fang, Yongchun Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

Morphology analysis based on atomic force microscopy (AFM) imaging contributes to understanding the characteristics of specimens more deeply. The preliminary and crucial step of implementing morphology analysis is to precisely segment the target area from the complex background. In this study, an automated AFM image segmentation strategy based on a well-designed U-shaped neural network is proposed to achieve accurate and robust segmentation for AFM images of different samples, thus realizing morphology analysis in micro-nano scale. Specifically, the centralized information interaction strategy cooperated with a two-path attention module is introduced to realize efficient cross-scale information interaction, which can fundamentally avoid the negative effects induced by spatial interpolation. Besides, the global information flows are adopted to guide the global information extracted by atrous spatial pyramid pooling to each level of the top-down pathway, which ensures that the high-level semantic information is not diluted during the top-down transmission process, thus locating the target area more precisely. Moreover, an AFM image dataset is constructed to train the network, which will be available online for free to facilitate other data-based AFM research. The segmentation results demonstrate that the proposed strategy has better performance on multiple AFM images compared with traditional Otsu method, fully convolutional network and U-Net. The application of the proposed method is carried out to exhibit the effectiveness in automated morphology analysis. Note to Practitioners-Despite the growing demand of AFM-based morphology analysis in many fields, the automated analysis is still lacking limited by accuracy and robustness of AFM image segmentation. Since manual segmentation, sometimes tedious and time-consuming, heavily depends on the personal judgment, it is thus necessary to develop automated segmentation methods. Although traditional automated segmentation al

关键词： Atomic force microscopy morphology analysis image segmentation deep learning U-shaped structure

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CVR-LSE: Compact Vectorized Representation of Local Static Environments for Reliable Obstacle Detection

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第8期71卷 9309-9318页

作者： Gao, Haiming Qiu, Qibo Hua, Wei Zhang, Xuebo Su, Zhiyi Zhang, Shun Zhejiang Lab Hangzhou 311121 Peoples R China Nankai Univ Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

This article proposes a compact vectorized representation approach to realize reliable obstacle detection for autonomous ground vehicles. Bridging the gap between the advantage of occupancy grid map in general obstacle detection and its shortcomings in memory-consuming and computational cost, a novel obstacle representation by multiple convex polygons is first proposed. Specially, to overcome the computational challenge brought by many 3-D point cloud, based on ground plane extraction and nearest obstacle selection, 3-D obstacle points are transformed into 2-D obstacle points. In addition, the maintenance process of grid map with a fixed range is proposed to further improve efficiency. More importantly, the compact obstacle representation is realized via multiple convex polygons through double threshold-based boundary simplification and convex polygon segmentation. The compact vectorized representation is the main contribution, which achieves the goal of compactness and accuracy on the premise of ensuring effective and reliable obstacle detection. The proposed approach has been applied in a practical autonomous driving project because of its superior performance on general obstacles detection. In addition, the quantitative evaluation shows the superior performance on making use of fewer number of points (decreased by about 50%) to represent local static environments.

关键词： Laser radar Reliability Vehicle dynamics Sensors Point cloud compression Autonomous vehicles Simultaneous localization and mapping Autonomous ground vehicle (AGV) compact vectorized representation convex polygon local static environment

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Hierarchical Context-Based Emotion Recognition With Scene Graphs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 3725-3739页

作者： Wu, Shichao Zhou, Lei Hu, Zhengxi Liu, Jingtai Nankai Univ Tianjin Key Lab Intelligent Robot & Inst Robot & Tianjin 300071 Peoples R China Nankai Univ Inst Robot & Automat Informat Syst Tianjin 300071 Peoples R China

For a better intention inference, we often try to figure out the emotional states of other people in social communications. Many studies on affective computing have been carried out to infer emotions through perceiving human states, i.e., facial expression and body posture. Such methods are skillful in a controlled environment. However, it often leads to misestimation due to the deficiency of effective inputs in unconstrained circumstances, that is, where context-aware emotion recognition appeared. We take inspiration from the advanced reasoning pattern of humans in perceived emotion recognition and propose the hierarchical context-based emotion recognition method with scene graphs. We propose to extract three contexts from the image, i.e., the entity context, the global context, and the scene context. The scene context contains abstract information about entity labels and their relationships. It is similar to the information processing of the human visual sensing mechanism. After that, these contexts are further fused to perform emotion recognition. We carried out a bunch of experiments on the widely used context-aware emotion datasets, i.e., CAER-S, EMOTIC, and BOdy Language Dataset (BoLD). We demonstrate that the hierarchical contexts can benefit emotion recognition by improving the accuracy of the SOTA score from 84.82% to 90.83% on CAER-S. The ablation experiments show that hierarchical contexts provide complementary information. Our method improves the F1 score of the SOTA result from 29.33% to 30.24% (C-F1) on EMOTIC. We also build the image-based emotion recognition task with BoLD-Img from BoLD and obtain a better emotion recognition score (ERS) score of 0.2153.

关键词： Emotion recognition Feature extraction Visualization Task analysis Face recognition Data mining Cognition Emotion recognition graph embedding hierarchical context scene graph

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STC-TEB: Spatial-Temporally Complete Trajectory Generation Based on Incremental Optimization

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IEEE robotICS AND AUTOMATION LETTERS 2025年第2期10卷 1289-1296页

作者： Zhu, Zeqing Zhang, Qianyi Song, Yinuo Yang, Yifan Liu, Jingtai Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Nankai Univ TBI Ctr Tianjin 300350 Peoples R China

In the context of indoor crowd navigation for mobile robots, the generation of spatial-temporally complete trajectories gives the robot a wider range of options, thereby improving the robustness and safety of navigation. Focused on this topic, this letter presents an incremental optimization framework that initially searches for complete trajectories in spatial dimension and subsequently optimizes them iteratively in spatial-temporal dimension. It benefits from a discount factor design to increase obstacle velocities incrementally, an adaptive determination of the discount factor step to ensure optimization robustness and a dragging force mechanism to adjust trajectories according to obstacle velocities at each step. Compared to the baseline algorithm EGO-TEB and Graphic-TEB, simulations and experiments conducted in lobbies and corridors, with both pedestrians and static obstacles, demonstrate that the proposed STC-TEB algorithm achieves the highest scenario success rate, the highest trajectory completeness rate, nearly the shortest time to the goal, and competitive optimization time.

关键词： Trajectory robots Optimization Pedestrians Collision avoidance Navigation Indexes Planning Robustness Force Human-aware motion planning crowd navigation spatial-temporally completeness incremental optimization collision avoidance

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Mee-SLAM: Memory efficient endoscopic RGB SLAM with implicit scene representation

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Zhou, Yuchen Li, Teng Dai, Yu Zhang, Jianxun Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China

Endoscopic dense simultaneous localization and mapping (SLAM) plays a critical role in robot assisted surgery. Recently, SLAM systems based on neural implicit representation have demonstrated superior localization and real-time mapping capabilities. However, due to the memory constraint in medical use, the lack of true depth in monocular endoscopy, and the existence of interferences in the working environment, dense reconstruction that balances quality and efficiency remain challenges. In this paper, we propose a globally consistent RGB SLAM system for simultaneous pose optimization and memory-efficient real-time reconstruction. Our method consists of three key components. To achieve better pose estimation, a visual odometry is designed to address distractors, and integrated with a loop closure. Online full bundle adjustment establishes a globally consistent map and reduces memory consumption through proposed keyframe management. Based on dynamically updated poses, a memory-efficient real-time rendering module is proposed to generate high quality dense maps. Evaluations on a public EndoSLAM dataset and areal endoscopic surgery dataset demonstrate that the proposed method achieves a superior balance inaccuracy and efficiency, laying the foundation for real-time application of surgical robots.

关键词： Endoscopy Simultaneous localization and mapping Implicit neural network

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