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Tele-rehabilitation of Upper Extremity with GARMI Robot: Concept and Preliminary Results

IFAC-PapersOnLine 2023年第2期56卷 3546-3551页

作者： Xiao Chen Hamid Sadeghian Yanan Li Sami Haddadin Munich Institute of Robotics and Machine Intelligence Technical University of Munich 80992 Munich Germany Department of Engineering and Design University of Sussex Brighton United Kingdom

The discrepancy between the growing need for rehabilitation and the inadequate availability of facilities and skilled therapists is becoming increasingly conspicuous. To address this problem, a tele-rehabilitation system of the upper extremity with the service robot GARMI is proposed. The system utilizes optimal control and game theory, considering the patient, GARMI and possibly the therapist as the agents who share a common cost function. By adapting the roles of these agents, the proposed system can provide passive, active, and assist-as-needed rehabilitation.

关键词： Active Rehabilitation Optimal control Impedance Control

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Two-Stream (2+1)D CNN Based on Frame Difference Attention for Driver Behavior Recognition 10

Two-Stream (2+1)D CNN Based on Frame Difference Attention fo...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Zou, Yang Du, Shaoyi Han, Hongcheng Liu, Yuying Tian, Zhiqiang Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Shaanxi Xi'an China Shunan Academy of Artificial Intelligence Zhejiang Ningbo China Xi'an Jiaotong University School of Software Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350304770

Driving behavior recognition is a key technology in human-machine cooperative driving, which plays an important role in the construction of an intelligent transportation environment. Due to the similarity of drivers ' actions and backgrounds in driving environments, it's challenging for existing behavior recognition methods to recognize behaviors in driving environments precisely. To solve these problems, we propose the two-stream (2+1)D CNN with Frame Difference Attention Module. First, we use two streams to process the information captured from the face camera and road camera and use the Squeeze and Excitation attention module to fuse the stream information. Second, we propose the Frame Difference Attention Module to enhance the changes between adjacent frames so that the subtle actions in driving environments can be captured easier. We test our network on the Brain4Cars dataset and have a good performance on it. © 2023 IEEE.

关键词： Cameras

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Investigation of Cryogenic Aging in 28 nm CMOS: Suppression of BTI and HCD in Circuits and SRAM

Investigation of Cryogenic Aging in 28 nm CMOS: Suppression ...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Diaz-Fortuny, Javier Benkhelifa, Mahdi Grill, Alexander Bury, Erik Degraeve, Robin Kaczer, Ben Amrouch, Hussam Imec Kapeldreef 75 Leuven3001 Belgium Technical University of Munich Munich Germany Tum School of Computation Information and Technology Munich Germany Ai Processor Design Munich Germany Munich Institute of Robotics and Machine Intelligence Munich Germany

ISBN: (纸本)9798331504779

The increasing interest in cryogenic circuits is driven by their transformative potential across high-performance computing, medical devices, space exploration, and quantum technologies. Operating transistors at cryogenic temperatures, such as 77 K and below, yields substantial improvements, including increased ON current, reduced OFF current, and enhanced sub-threshold slope, paving the way for significant gains in efficiency. While recent studies have explored device-level reliability at cryogenic temperatures, circuit-level reliability-particularly under bias temperature instability (BTI) and hot carrier degradation (HCD)-remains underexamined, leaving critical aging mechanisms at these temperatures poorly understood. To bridge this gap, we designed and fabricated a fully customized chip in a commercial 28 nm technology. The chip integrates diverse ring oscillator (RO) circuits for precise characterization of BTI and HCD aging effects, enabling evaluation of their impact on performance at cryogenic temperatures as low as 4 K. This work ensures that as cryogenic CMOS technologies drive innovation in critical applications, their reliability is comprehensively understood and advanced. © 2025 IEEE.

关键词： CMOS integrated circuits

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Assessing Perceived Discomfort and Proxemic Behavior towards Robots: A Comparative Study between Real and Augmented Reality Presentations

Assessing Perceived Discomfort and Proxemic Behavior towards...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Olivia Herzog Simone Nertinger Katharina Wenzel Abdeldjallil Naceri Sami Haddadin Klaus Bengler Chair of Ergonomics School of Engineering and Design Technical University of Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany

This paper assesses the usefulness of immersive technology to evaluate perceived discomfort and proxemic behavior towards a robot depending on its size. Therefore, we compared a real and an augmented reality (AR) robot presentation. In a within-subject design, a service humanoid approached participants (N = 32) in four trials in a counterbalanced order. One trial presented a real robot, another showed a same-sized AR version, and two trials showed down-scaled AR versions of the robot. The perceived discomfort and the comfort distance were measured. For the presentation mode comparison, the distance estimation error was measured additionally. The results show that the comfort distance was greater for the AR robot than for the real one. The comfort distance was also greater for the largest robot compared to the smaller sizes, while there was no difference when comparing the smaller ones. There was no difference in perceived discomfort between the presentation modes or the robot sizes. The distance estimation error was greater in AR. The study indicates that results obtained with the AR and real robot are comparable relative to each other. Therefore, utilizing AR could effectively evaluate various versions of robots in terms of the discomfort they induce—a critical prerequisite prior to the manufacturing process. Finally, AR might provide better validity for the evaluation of subjective measures.

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Vision-driven Collaborative Mobile Robotic Human Assistant System for Daily Living Activities

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IFAC-PapersOnLine 2023年第2期56卷 4400-4405页

作者： Yuankai Wu Rayene Messaoud Xiao Chen Arne-Christoph Hildebrandt Marco Baldini Constantin Patsch Hamid Sadeghian Sami Haddadin Eckehard Steinbach Technical University of Munich School of Computation Information and Technology Department of Computer Engineering and Munich Institute of Robotics and Machine Intelligence (MIRMI) ABB

Assistive robotics is a rapidly growing research area. They are increasingly in demand in industry, medical services, or even domestic environments. Many assistance systems were developed in the last decades to help with industry tasks mainly. However, the majority of assistive robotic systems can only execute commands according to pre-defined instructions and are not capable of social interaction with humans. To this end, a human-centered assistive robotic system that can automatically assist or completely help humans to accomplish their intentions is expected. In this paper, we first proposed a novel triplet concept that describes the intended task of humans but will be executed by the assistive robot. The triplet includes target object, subject and action enabling the robot to identify auxiliary subjects, desired objects and perform required actions. Furthermore, a vision-driven mobile robotic human assistant system for assisting humans in daily life tasks is developed. The proposed triplet was proven to be able to complete a fully autonomous robotic system as a predefined input guide. Sensor calibration, deep learning based object detection, manipulation, and grasping techniques are used to achieve this goal. We evaluated the proposed system using various simulation and experimental scenarios and confirmed its effectiveness.

关键词： Autonomous robotic systems Human-centred automation and design Perception and sensing Assistive robot

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Safety-Critical Scenario Generation by Causal Influence Detection 27

Safety-Critical Scenario Generation by Causal Influence Dete...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Yang, Yibing Zhang, Chi Xu, Linhai Ma, Shuangxun Li, Li Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Shaanxi Xi'an China School of Future Transportation Chang'an University Xi'an710064 China Tsinghua University BNRist Department of Automation Beijing100084 China

ISBN: (纸本)9798331505929

Appropriate behavior intervention for a specific background vehicle at the right moment is a crucial step in identifying safety-critical scenarios. Existing methods generate scenarios by estimating the data distribution, but when noise interventions are present, the emergence of safety-critical scenarios becomes colinearity with the noise interventions, potentially resulting in unnecessary interventions and unrealistic vehicle behaviors. To address this, we propose to integrate the causal relationship between the background vehicle behavior and the safety status of ego-vehicle as a prior into scenario generation. In this notion, we present a reinforcement learning framework with a causal influence detection module (CausalID). Specifically, we employ conditional mutual information to quantify the causal influence between vehicle sequential behaviors, and intervene on the background vehicle's behavior when the causal influence exceeds a certain threshold. Additionally, to prevent unrealistic collision-oriented driving, we sample the final intervention action from a set of candidate actions according to their probabilities of occurring in natural driving scenarios. Our experiments in 3-lane highway scenarios with multiple vehicles validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Vehicle safety

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Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality

Three-Dimensional Vehicle Dynamics State Estimation for High...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sven Goblirsch Marcel Weinmann Johannes Betz Department of Mobility Systems Engineering TUM School of Engineering and Design Technical University of Munich Garching Germany Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University of Munich Garching Germany Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work aims to present a three-dimensional vehicle dynamics state estimation under varying signal quality. Few researchers have investigated the impact of three-dimensional road geometries on the state estimation and, thus, neglect road inclination and banking. Especially considering high velocities and accelerations, the literature does not address these effects. Therefore, we compare two- and three-dimensional state estimation schemes to outline the impact of road geometries. We use an Extended Kalman Filter with a point-mass motion model and extend it by an additional formulation of reference angles. Furthermore, virtual velocity measurements significantly improve the estimation of road angles and the vehicle’s side slip angle. We highlight the importance of steady estimations for vehicle motion control algorithms and demonstrate the challenges of degraded signal quality and Global Navigation Satellite System dropouts. The proposed adaptive covariance facilitates a smooth estimation and enables stable controller behavior. The developed state estimation has been deployed on a high-speed autonomous race car at various racetracks. Our findings indicate that our approach outperforms state-of-the-art vehicle dynamics state estimators and an industry-grade Inertial Navigation System. Further studies are needed to investigate the performance under varying track conditions and on other vehicle types.

关键词： Temperature measurement Geometry Global navigation satellite system Roads Banking Prediction algorithms Velocity measurement Automobiles State estimation Vehicle dynamics

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Unified regularity measures for sample-wise learning and generalization

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Visual intelligence 2024年第1期2卷 1-20页

作者： Zhang, Chi Yuan, Meng Ma, Xiaoning Liu, Yu Lu, Haoang Wang, Le Su, Yuanqi Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 China National KeyLaboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an 710049 China National Engineering Research Center for Visual Information and Applications Xi’an Jiaotong University Xi’an 710049 China School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China

Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes. Recent studies on deep neural networks (DNNs) suggest that such sample differences are rooted in the distribution of intrinsic pattern information, namely sample regularity. Motivated by recent discoveries in network memorization and generalization, we propose a pair of sample regularity measures with a formulation-consistent representation for both processes. Specifically, the cumulative binary training/generalizing loss (CBTL/CBGL), the cumulative number of correct classifications of the training/test sample within the training phase, is proposed to quantify the stability in the memorization-generalization process, while forgetting/mal-generalizing events (ForEvents/MgEvents), i.e., the misclassification of previously learned or generalized samples, are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics. The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent (SGD) optimization are validated through sample-wise analyses. Further training/test sample selection applications show that the proposed measures, which share the unified computing procedure, could benefit both tasks. © The Author(s) 2024.

关键词： Forgetting events Memorization-generalization mechanism Network generalization Sample regularity Sample reweighting

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A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

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Optimizing Autonomous Vehicle Sensor Setups: A Framework for Coverage Analysis

Optimizing Autonomous Vehicle Sensor Setups: A Framework for...

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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Philipp Hafemann Simon Enrico Hahn Markus Lienkamp Department of Mobility Systems Engineering Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany School of Engineering & Design Munich Germany

The performance of environment perception in autonomous vehicles is significantly influenced by the sensor setup, which is determined in the early design phase. The selection of the type and position of sensors in this phase often occurs before the availability of data processing algorithms. Therefore, the evaluation of the sensor setup can only be based on coverage metrics. This paper presents a novel framework for modeling the sensor coverage of autonomous vehicles. By discretizing the environment into grid cells, our framework analyzes each sensor and the whole setup. Our methodology systematically determines the coverage, identifies redundancy and blind spot areas, and obtains quantifiable metrics for evaluating the sensor setup efficiency. Utilizing the PyVista visualization library, we present the individual building blocks of the framework and their open-source implementation. The results from a real-world case study demonstrate our framework’s ability to identify weaknesses in sensor setup coverage. Our approach helps to develop a comprehensive understanding of the sensor coverage and, therefore, contributes to designing more effective and reliable sensing systems.

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