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arXiv 2024年

作者： Kirschner, Robin Jeanne Micheler, Carina M. Zhou, Yangcan Siegner, Sebastian Hamad, Mazin Glowalla, Claudio Neumann, Jan Rajaei, Nader Burgkart, Rainer Haddadin, Sami Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80992 Germany Department of Orthopaedics and Sports Orthopaedics Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching near Munich85748 Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau82418 Germany Department of Diagnostic and Interventional Radiology Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries. © 2024, CC BY.

关键词： Risk assessment

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Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees

Physics-Model-Regulated Deep Reinforcement Learning Towards ...

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IEEE Conference on Decision and Control

作者： Hongpeng Cao Yanbing Mao Lui Sha Marco Caccamo Hongpeng Cao is with School of Engineering and Design Technical University of Munich Munich Germany Yanbing Mao is with Engineering Technology Division Wayne State University Detroit MI USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA School of Engineering and Design and Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-model-regulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-model-based control and data-driven control. The concurrent designs enable the Phy-DRL the mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.

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Genetic Informed Trees (GIT*): Path planning via reinforced genetic programming heuristics

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Biomimetic intelligence and robotics 2025年

作者： Liding Zhang Kuanqi Cai Zhenshan Bing Chaoqun Wang Alois Knoll Chair of Robotics Artificial Intelligence and Real-time Systems TUM School of Computation Information and Technology (CIT) Technical University of Munich Munich 85748 Germany Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich 80992 Germany School of Control Science and Engineering Shandong University Jinan 250100 China

Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from R 4 to R 16 and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at A video showcasing our experimental results is available at https://***/URjXbc_BiYg .

关键词： Genetic algorithm Reinforced genetic programming Generative heuristics Optimal path planning

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Analysis and Characterization of Defects in FeFETs

Analysis and Characterization of Defects in FeFETs

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IEEE International Test Conference

作者： Dhruv Thapar Simon Thomann Arjun Chaudhuri Hussam Amrouch Krishnendu Chakrabarty Department of Electrical and Computer Engineering Duke University North Carolina USA Chair of AI Processor Design Technical University of Munich (TUM) Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany School of Electrical Computer and Energy Engineering at Arizona State University (ASU) Arizona USA

Emerging devices are susceptible to manufacturing defects due to immature fabrication processes. Ferroelectric field-effect transistors, referred to as FeFETs, are promising emerging devices, but the impact of manufacturing imperfections on these devices has yet to be studied. Thus, we combine a technology CAD (TCAD) model with a fault-injection technique to represent fabrication defects in a FeFET. The TCAD model is calibrated against a fabricated metal-ferroelectric-metal capacitor and uses a multi-domain ferroelectric-layer structure. We address two classes of defects in the ferroelectric layer and map them to stuck-at-fault models referred to as neutral faults (SAP°) and stuck-at-plus and stuck-at-minus (SAP + and SAP − ) faults. We also develop a machine-learning (ML) framework to characterize these fault-injected FeFET devices. The ML framework provides a significant speedup in predicting the health of the FE layer as compared to computationally heavy TCAD simulations. Our study of defects in ferroelectric FET (FeFET), which is done for the first time, and the insights gained thereof can provide valuable feedback for the fabrication and yield learning of FeFET-based circuits.

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Correction to: On the use of adjoint gradients for time-optimal control problems regarding a discrete control parameterization

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Multibody system Dynamics 2023年第3期59卷 335-336页

作者： Lichtenecker, Daniel Rixen, Daniel Eichmeir, Philipp Nachbagauer, Karin TUM School of Engineering and Design Department of Mechanical Engineering Chair of Applied Mechanics Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany Institute of Mechanics and Mechatronics Vienna University of Technology Wien Austria Faculty of Engineering and Environmental Sciences University of Applied Sciences Upper Austria Wels Austria Institute for Advanced Study Technical University of Munich Garching Germany

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To Sparsify or not to Sparsify: Simplifying Visual Feature Maps for Mobile Agents

To Sparsify or not to Sparsify: Simplifying Visual Feature M...

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IEEE International Symposium on Multimedia

作者： Sebastian Eger Rastin Pries Gábor Sörös Michael G. Adam Martin Piccolrovazzi 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 Nokia Bell Labs Munich Germany Nokia Bell Labs Budapest Hungary

ISBN: (纸本)9781665471732

Real-time pose estimation is crucial for autonomous agents for motion control and navigation, and it is essential for extended reality applications as well. As the current global positioning systems are not reliable indoors or are not precise enough, visual simultaneous localization and mapping is becoming prevalent in autonomous agents and mobile devices. The visual feature maps can be shared between agents and might be merged into a common map on a server and redistributed to all clients to enable re-localization and co-localization. However, merged maps can grow continuously in density and size that constrained agents are not able to handle *** this paper, we investigate how the map density effects localization and visual odometry performance on low-cost mobile clients. We show that a sparse representation of the original VFM is necessary for real-time visual odometry but at the cost of a degraded re-localization performance.

关键词： Visualization Simultaneous localization and mapping Mobile agents Pose estimation Real-time systems Mobile handsets Autonomous agents

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Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET

arXiv

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arXiv 2023年

作者： Zhou, Yifei Huang, Xuchu Yang, Jianyi Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Department of Electrical Engineering University of Notre Dame Notre Dame United States AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as"memory wall" issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION/IOFF ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0°C to 85°C, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T-1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Assessing the robustness of an optimization method for estimating muscle activity during gait: Preliminary findings

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Gait & Posture 2024年 113卷 135-135页

作者： Gheorghe Lisca Kim Kristin Peper Adam Park Thomas Grauschopf Veit Senner Sami Haddadin TUM School of Engineering and Design Associate Professorship of Sport Equipment and Sport Materials Munich Germany Audi Konfuzius-Institut Ingolstadt Microlab Ingolstadt Germany Technische Hochschule Ingolstadt Informatik Ingolstadt Germany Technical University of Munich Munich Institute of Robotics and Machine Intelligence- Chair of Robotics and System Intelligence Munich Germany

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Low Power and Temperature- Resilient Compute-In-Memory Based on Subthreshold-FeFET

Low Power and Temperature- Resilient Compute-In-Memory Based...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yifei Zhou Xuchu Huang Jianyi Yang Kai Ni Hussam Amrouch Cheng Zhuo Xunzhao Yin Zhejiang University Hangzhou China ZJU - Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Department of Electrical Engineering University of Notre Dame Notre Dame USA Chair of AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as “memory wall” issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION / $I$ O F F ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0°C to 85°C, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W.

关键词： Semiconductor device modeling Power demand Nonvolatile memory Microprocessors Neural networks Computer architecture Energy efficiency

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Learning vision-based reactive policies for obstacle avoidance

arXiv

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arXiv 2020年

作者： Aljalbout, Elie Chen, Ji Ritt, Konstantin Ulmer, Maximilian Haddadin, Sami Munich School of Robotics and Machine Intelligence Technical University of Munich Germany

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate, while maintaining closed-loop responsiveness required for critical applications like human-robot interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer vision

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