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Annual International Symposium on Reliability Physics

作者： Javier Diaz-Fortuny Mahdi Benkhelifa Alexander Grill Erik Bury Robin Degraeve Ben Kaczer Hussam Amrouch imec Leuven Belgium Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

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.

关键词： Degradation Semiconductor device measurement Voltage measurement Accelerated aging Cryogenics Hot carriers CMOS technology Frequency measurement Integrated circuit reliability Stress

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Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety

Overcoming Blind Spots: Occlusion Considerations for Improve...

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IEEE Symposium on Intelligent Vehicle

作者： Korbinian Moller Rainer Trauth Johannes Betz 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) Institute of Automotive Technology Technical University of Munich Garching Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle’s trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planning algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://***/TUM-AVS/Frenetix-Occlusion.

关键词： Uncertainty Trajectory planning Software algorithms Urban areas Prediction algorithms Safety Trajectory

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Latent Action Priors for Locomotion with Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Hausdörfer, Oliver von Rohr, Alexander Lefort, Éric Schoellig, Angela P. The Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Learning Systems and Robotics Lab Germany Munich Institute of Robotics and Machine Intelligence Germany

Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation leads to a closer replication of the expert’s behavior. Videos and code are available at https://***/view/ latent-action-priors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit

Real-Time-Capable Muscle Force Estimation for Monitoring Rob...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kim K. Peper Alexander Aasmann Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic rehabilitation therapy in the ICU. This method is solely based on sensor information provided by the rehabilitation robot. In current clinical practice, monitoring primarily takes place in the later stages of rehabilitation, but it would also be highly beneficial during early stages. Musculoskeletal models have large, mostly unrealized potential to support and improve patient monitoring. The method presented in this paper is based on a state-of-the-art muscle-tendon path model, which is applied to the use case of the robotic rehabilitation device VEMOTION. The muscle force estimation is validated against surface electromyography measurements of lower limb muscles from 12 healthy volunteers The results show an overall correlation of R = 0.70 0.25 for the single-joint muscle m. iliopsoas, which has a ±major contribution to hip flexion. Given this correlation, the proposed model could be used for real-time monitoring of active patient participation.

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Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility

Estimating Joint Kinematics and Muscles Forces During Roboti...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Kim K. Peper Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation $(R=0.88\pm 0.09)$ between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements $(R\ =\ 0.62\pm 0.27)$ are promising and a first application to a patient data set shows potential for future clinical application.

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Determination of the Characteristics of Gears of Robot-Like Systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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RFOOD: Real-time Facial Authentication and Out -of-distribution Detection with Short-range FMCW Radar

RFOOD: Real-time Facial Authentication and Out -of-distribut...

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International Conference on machine Learning and Applications (ICMLA)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Boran Hamdi Sivrikaya Eckehard Steinbach Department of Computer Engineering Technical University of Munich School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Out-of-distribution (OOD) detection is critical for the safe deployment of modern neural network architectures, as it aims to identify samples outside the training domain. In this paper, we introduce RFOOD, a novel OOD detection framework designed for real-time, privacy-preserving facial authentication using low-cost frequency-modulated continuous-wave (FMCW) radar. RFOOD employs both range-Doppler and micro range- Doppler images to enhance the detection accuracy. The architecture consists of a multi-encoder multi-decoder Body Part (BP) and Intermediate Linear Encoder-Decoder (ILED) components. This design allows the system to accurately classify a single individual's face as in-distribution (ID) while identifying all other faces as OOD. On our dataset collected with 60 GHz short-range FMCW radar, RFOOD achieves an Area Under the Receiver Operating Characteristic (AUROC) curve of 94.13 % and a False Positive Rate of 18.12% at a True Positive Rate of 95 % (FPR95). Additionally, RFOOD outperforms state-of-the-art OOD detection methods in common OOD detection metrics and operates in real-time.

关键词： Measurement Training Accuracy Neural networks Radar detection Authentication Receivers Feature extraction Real-time systems Faces

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Long-Term Action Anticipation Based on Contextual Alignment

Long-Term Action Anticipation Based on Contextual Alignment

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Constantin Patsch Jinghan Zhang Yuankai Wu Marsil Zakour Driton Salihu Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) School of Computation Information and Technology Technical University of Munich

In action anticipation, the model predicts the next future action after a certain observation period. In long-term action anticipation, this idea is further extended to predicting multiple actions and their respective duration. Thus, in this problem setting the model should not only capture relationships between past actions but also predict several future actions that fit into a certain context. Compared to autoregressive models, our model employs an encoder decoder structure to determine future actions and durations in parallel, which prevents the accumulation of prediction errors and reduces the inference time. Furthermore, it is ensured that the predicted actions are aligned with respect to a context representation, which resembles the way humans approach this task as the feasible action set is restricted by the respective context. We evaluate our model on the long-term anticipation benchmark datasets, Breakfast, and 50Salads, where we achieve state-of-the-art results.

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Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database

Real-Time UAV Fault Detection and Classification Using Measu...

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IEEE/SICE International Symposium on System Integration

作者： Radosław Puchalski Wojciech Giernacki Quang Ha Faculty of Control Robotics and Electrical Engineering of Poznan Institute of Robotics and Machine Intelligence University of Technology Poznan Poland Faculty of Engineering and Information Technology School of Electrical and Data Engineering University of Technology Sydney Ultimo NSW Australia

The growing number of applications for unmanned aerial vehicles operating in close proximity to humans calls for strict safety requirements. To ensure reliability and safety, fast and effective diagnosis of possible drone faults is needed. In this paper, a study on the detection of damage to the propellers of a multirotor using inertial sensors was conducted. Measurement data from the publicly available PADRE repository were used. Frequency features and raw measurement data applicability were tested. Two different approaches to data processing during fault detection and classification were tested. The first is to analyze the signals after collecting the entire set of data needed for treatment by the neural network. The second approach processes the data after every single acquisition of sensor measurements. The performance results and processing time of each solution are recorded for analysis. By effectively selecting parameters of the proposed approaches, the processing time and accuracy of UAV fault classification can be significantly improved, as verified over different classes of propeller defects. Early fault detection is essential for safe operations of multirotor drones.

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IMPS: Informative Map Point Selection for Visual-Inertial SLAM

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5595-5605页

作者： Changxiang Liu Hongshan Yu Qiang Fu Xieyuanli Chen Naveed Akhtar Zhi-Hong Mao College of Electrical and Information Engineering School of Robotics Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University Changsha China National University of Defense Technology Changsha China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

Visual-inertial SLAM (VINS) stands at the forefront of advancements in computer vision, robotics, and autonomous driving, revolutionizing the way we perceive and navigate the world. The typical approach in optimization-based methods for VINS is to estimate camera poses by minimizing the reprojection errors of all corresponding map points. However, not all map points are suitable for accurate pose estimation. Some map points introduce noise and can compromise the accuracy of the estimation. Current methods primarily focus on removing 2D noise points, which proves to be ineffective since triangulation can increase the uncertainty of map points. To address this problem, we propose a new method for Informative Map Point Selection (IMPS) in VINS systems. IMPS identifies the most informative map points by utilizing mutual and geometric information as the optimization target. We integrate IMPS into a VINS system and evaluate its performance using publicly available EuRoC, TUM and M2DGR datasets, as well as our own data. Experimental results demonstrate that our method surpasses existing VINS methods and achieves state-of-the-art pose estimation performance. Importantly, IMPS functions as an independent module with strong generalization capabilities, allowing for easy integration into other VINS systems and enhancing pose estimation performance. This integration not only elevates pose estimation accuracy but also holds great promise for advancing applications in intelligent driving and unmanned systems.

关键词： Simultaneous localization and mapping Optimization Cameras Pose estimation Robots Mutual information Intelligent vehicles

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