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

作者： Lars Nockenberg Wenxuan Wei Mariam Navai Eckehard Steinbach School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present a vibrotactile codec based on a convolutional neural network that is fully rate-scalable and perceptually optimized. Rate-scalability so far was a rarely addressed problem with deep learning-based codecs. This is achieved through a bit allocation algorithm that perceptually optimizes the signal quantization, utilizing bitrate estimation based on a gaussian entropy model. We compare different bit allocation approaches regarding signal quality and found that our algorithm is effective at reaching the desired bitrate while achieving higher performance than our previously designed classical codec.

关键词： Codecs Quantization (signal) Scalability Bit rate Signal processing algorithms Estimation Entropy Iterative methods Convolutional neural networks Speech processing

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R²NMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control

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IFAC-PapersOnLine 2024年第18期58卷 309-316页

作者： Baha Zarrouki João Nunes Johannes Betz Chair of Automotive Technology Technical University Munich Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University Munich Munich Institute of Robotics and Machine Intelligence (MIRMI)

In this paper, we present a novel Reduced Robustified NMPC (R²NMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets. This promises both a closed-loop- and constraint satisfaction performance equivalent to common Robustified NMPC approaches while drastically reducing the computational complexity. The main idea lies in approximating the ellipsoidal uncertainty sets propagation over the prediction horizon with the system dynamics’ sensitivities inferred from the last optimal control problem (OCP) solution and similarly for the gradients to robustify the constraints. Thus, we do not require the decision variables related to the uncertainty propagation within the OCP, rendering it computationally tractable. Next, we illustrate the real-time control capabilities of our algorithm in handling a complex, high-dimensional, and highly nonlinear system, namely the trajectory following of an autonomous passenger vehicle modeled with a dynamic nonlinear single-track model. Our experimental findings, alongside a comparative assessment against other Robust NMPC approaches, affirm the robustness of our method in effectively tracking an optimal racetrack trajectory while satisfying the nonlinear constraints. This performance is achieved while fully utilizing the vehicle's interface limits, even at high speeds of up to 37.5m s −1 , and successfully managing state estimation disturbances. Our approach maintains a mean solving frequency of 140Hz. The code used in this research is publicly accessible as open-source software: https://***/bzarr/TUM-CONTROL

关键词： Robust control Nonlinear predictive control Real-time optimal control Nonlinear and optimal automotive control Adaptive and robust control of automotive systems

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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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Non-planar Printing for Stretchable Tactile Sensors

Non-planar Printing for Stretchable Tactile Sensors

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IEEE International Conference on Flexible and Printable Sensors and systems (FLEPS)

作者： Sonja Gros Michael Tim Braun Amartya Ganguly Sami Haddadin Technical University of Munich Germany TUM School of Computation Information and Technology (CIT) Chair of Robotics and Systems Intelligence (RSI) Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet with Human-in-the-Loop (CeTI)

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Tactile sensors are gaining significance in numerous research fields, encompassing prosthetics, human-robot interaction, and manipulation. Recent advancements in soft and stretchable tactile skin for diverse applications impose an increasing demand for advanced printing methods to deploy such skins on curved surfaces like fingers or robot bodies. Our study introduces a novel non-planar printing method for tactile sensors on curved surfaces. This includes the design of a silicone and liquid conductive ink-based sensor, its manufacturing with our novel transformation that utilizes a liquid multi-material printer setup, and the sensor characterization under various conditions. Our findings demonstrate the feasibility of printing skin-like force sensors directly onto curved objects, with potential applications in the aforementioned research domains.

关键词： Printing Liquids Fingers Tactile sensors Human-robot interaction Skin Manufacturing

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Rethinking 3D Geometric Object Features for Enhancing Skeleton-based Action Recognition

Rethinking 3D Geometric Object Features for Enhancing Skelet...

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

作者： Yuankai Wu Chi Wang Driton Salihu Constantin Patsch Marsil Zakour Eckehard Steinbach School of Computation Information and Technology / Chair of Media Technology and Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Human action recognition is crucial for intelligent robots, especially in the realm of human-robot collaboration research. Recent advancements in human pose estimation algorithms have shifted the focus of action recognition towards skeleton-based models, which exhibit robustness to changes in background and illumination. However, many state-of-the-art action recognition models rely on 2D skeleton data, neglecting object features. This limitation becomes obvious in complex scenarios where human interactions with objects are crucial, potentially compromising the reliability of assistive robots in understanding human behavior in their environment. To address this issue, we propose a method that effectively integrates 3D geometric object features into skeleton data using graph convolutional neural networks (GCNs). In addition to analyzing the effectiveness of information from different dimensions such as object center position, category, translation, and rotation, we explore various adjacency matrix designs for graph networks. Our model performance is evaluated on two challenging datasets: IKEA ASM and Bimanual Actions. The results demonstrate a significant improvement in action recognition by integrating object features into skeleton-based models. Specifically, on the IKEA-ASM dataset, our approach achieves a frame-wise Top-1 score improvement of 10.8% and an average F1@k improvement of 13.3%, while on the Bimanual Actions dataset, it achieves a frame-wise Top-1 score improvement of 11.4% and an average F1@k improvement of 5.3%, with negligible increases in model complexity.

关键词： Solid modeling Three-dimensional displays Translation Pose estimation Lighting Human-robot interaction Skeleton Robustness Human activity recognition Intelligent robots

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Kolmogorov-Arnold Network for Transistor Compact Modeling

arXiv

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arXiv 2025年

作者： Novkin, Rodion Amrouch, Hussam 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

Neural network (NN)-based transistor compact modeling has recently emerged as a transformative solution for accelerating device modeling and SPICE circuit simulations, particularly at advanced technology nodes, where the development cycle for mature physics-based models becomes very extensive. However, conventional NN architectures, despite their widespread adoption in state-of-the-art methods, primarily function as black-box problem solvers. This lack of interpretability significantly limits their capacity to extract and convey meaningful insights into learned data patterns, posing a major barrier to their broader adoption in critical modeling tasks. This work introduces, for the first time, Kolmogorov-Arnold network (KAN) for the transistor - a groundbreaking NN architecture that seamlessly integrates interpretability with high precision in physics-based function modeling. We systematically evaluate the performance of KAN and Fourier KAN (FKAN) for fin field-effect transistor (FinFET) compact modeling, benchmarking them against the golden industry-standard compact model and the widely used multi-layer perceptron (MLP) architecture. Our results reveal that KAN and FKAN consistently achieve superior prediction accuracy for critical figures of merit, including gate current (ID), drain charge (QD), and source charge (QS). Furthermore, we demonstrate and improve the unique ability of KAN to derive symbolic formulas from learned data patterns - a capability that not only enhances interpretability but also facilitates in-depth transistor analysis and optimization. This work highlights the transformative potential of KAN in bridging the gap between interpretability and precision in NN-driven transistor compact modeling. By providing a robust and transparent approach to transistor modeling, KAN represents a pivotal advancement for the semiconductor industry as it navigates the challenges of advanced technology scaling. © 2025, CC BY.

关键词： SPICE

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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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Accelerating Virtual Fixture Estimation for Robot Manipulation using Reinforcement Learning and Human Demonstrations

Accelerating Virtual Fixture Estimation for Robot Manipulati...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Diego Fernandez Prado Konstantinos Larintzakis Jan Irsperger Eckehard Steinbach Information and Technology / Chair of Media Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich / School of Computation Munich Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Teleoperation has the potential to greatly simplify the programming of industrial robots by allowing experts to teach tasks from remote. In order to facilitate the accomplishment of a particular task, Virtual Fixtures (VFs) are often added to the teleoperation system, but designing them is a tedious process that requires time and expertise. In this paper, human teleoperated demonstrations are combined with Proximal Policy Optimization (PPO) to decrease the convergence time of a Reinforcement Learning (RL) algorithm that automatically estimates the parameters of a VF for a Pegin-Hole insertion task. The acceleration of the convergence allows us to also estimate parameters of more intricate VFs, where the RL algorithm did not converge otherwise. The experiments show that this technique can successfully infer the expected parameters of predefined VFs in a third of the time compared to the state-of-the-art approach. Additionally, the learned behaviour of the robot manipulator in simulation is executed on a robot arm using an impedance controller to demonstrate that the virtual system closely replicates its real counterpart, and that the learned VFs have higher chances of being useful to a user controlling the real system.

关键词： Fixtures Estimation Reinforcement learning Programming Manipulators Industrial robots Trajectory Time factors Optimization Convergence

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Designing Humanoids: How Robot Posture Influences Users’ Perceived Safety in HRI

Designing Humanoids: How Robot Posture Influences Users’ Pe...

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IEEE-RAS International Conference on Humanoid Robots

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

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

In human-human interaction, posture serves as a critical non-verbal cue that subconsciously shapes first impressions and perceptions. Given the preference for anthropomorphism of robots acting in socially intensive situations such as caregiving, the influence of posture is expected to increase in interactions with humanoid robots. This study aims to identify the most preferred default position for the assistive humanoid robot GARMI, ensuring users’ perceived safety in human-robot interaction (HRI). In a preliminary study, 30 participants evaluated ten different arm postures of the robot GARMI regarding their perceived discomfort. From these evaluations and direct rankings, three arm positions were selected for further analysis alongside the current default position in the virtual reality (VR) study. In this subsequent study, 50 participants assessed their perception of safety using both objective measures of comfort distance, grounded in proxemic theory, and two subjective measures, i.e., Godspeed questionnaire and Robotic Social Attribute Scale (RoSaS). The results indicate a significant impact of the robot’s arm postures on users’ perceived safety. A polite, butler-like posture is recommended as the default position, aligning with the role users typically attribute to the robot GARMI.

关键词： Atmospheric measurements Shape Current measurement Fitting Humanoid robots Human-robot interaction Virtual reality Particle measurements Manipulators Safety

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