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IEEE International Symposium on Multimedia

作者： Driton Salihu Adam Misik Markus Hofbauer Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Siemens Technology Munich Germany

ISBN: (纸本)9781665471732

Scan-to-CAD-based 3D reconstruction of indoor environments has become increasingly more popular in recent years. The inherent structure of Scan-to-CAD consists of object detection, model retrieval, and alignment. Therefore, a variety of metrics are required to assess these three aspects. This can lead to ambiguous evaluation results and incorrect quality assumptions. To impede the problem of incorrect evaluation, we introduce S2CMAF, a multi-method assessment fusion approach for Scan-to-CAD pipelines. S2CMAF merges several metrics used in evaluating these pipelines into one unique quality score. We show that S2CMAF significantly improves the correlation between Scan-to-CAD results and the ground truth, compared to the conventionally used Scan2CAD benchmark. Additionally, we train S2CMAF using different optimization techniques and demonstrate the advantages of our approach on real-world data.

关键词： Measurement Point cloud compression Correlation Three-dimensional displays Pipelines Object detection Benchmark testing

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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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TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design

arXiv

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arXiv 2025年

作者： Zhou, Yifei Kämpfe, Thomas Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Fraunhofer IPMS Dresden Germany Department of Electrical Engineering University of Notre Dame Notre Dame United States 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 China

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the"memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0°C to 85°C at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Reactive Cooperative Manipulation based on Set Primitives and Circular Fields

Reactive Cooperative Manipulation based on Set Primitives an...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Riddhiman Laha Luis F.C. Figueredo Juraj Vrabel Abdalla Swikir Sami Haddadin Munich School of Robotics and Machine Intelligence Technische Universität München (TUM) Munich Germany

This paper addresses the problem of real-time planning in constrained dual-arm manipulation scenarios. Our proposed coupling leverages manipulability information of the cooperative bimanual task-space to a vector-field based planner by means of a repulsive circulatory field, while geometric primitives in Spin(3)⋉ℝ³ are explored for flexible task relaxation. Furthermore, the circular field informs the cooperative framework about the safety boundaries which are in turn used to further relax motion constraints within a collision-free ball in Cartesian space. This builds a funnel along the trajectory which can be directly tracked through the proposed switching of task-primitive-priorities. The switching strategy follows an approach that ensures robustness to chattering and continuity in the joint-space. Experiments verify that our framework can run within the inner control loop of Franka Emika Panda robots.

关键词： Couplings Tracking Switches Real-time systems Robustness Trajectory Safety

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Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty

arXiv

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arXiv 2023年

作者： Gazar, Ahmad Khadiv, Majid Del Prete, Andrea Righetti, Ludovic Max Planck Institute for Intelligent Systems Tuebingen Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Industrial Engineering Department University of Trento Italy Tandon School of Engineering New York University New York United States

Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for control and the real robot dynamics. This induces control policies that could violate the contact location constraints by making contact at unintended locations, leading to unsafe motion plans. This work addresses the problem of robust kino-dynamic whole-body trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance constraints as a function of the robot’s full kinematics. We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for whole-body trajectory optimization in terms of contact location constraint satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped robot performing agile trotting and bounding motions over small stepping stones, where contact location satisfaction becomes critical. Our results show that SNMPC can perform all motions safely with 100% success rate, while NMPC failed 48.3% of all motions. © 2023, CC BY.

关键词： Stochastic systems

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Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS to Cryogenic Computing

Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS ...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Benkhelifa, Mahdi Parihar, Shivendra S. Kar, Anirban Pahwa, Girish Chauhan, Yogesh S. Amrouch, Hussam Technical University of Munich Tum School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Ai Processor Design Munich Germany University of California Department of Electrical Engineering and Computer Sciences Berkeley United States National Yang Ming Chiao Tung University International College of Semiconductor Technology Taiwan Indian Institute of Technology Kanpur India

ISBN: (纸本)9783982674100

As CMOS scaling approaches its fundamental limits, the explosive rise of AI and LLMs has unveiled profound bottlenecks in computing architectures. This paper presents two groundbreaking paradigms poised to reshape the landscape of high-performance computing and meet the surging demands of AI-driven workloads. The first paradigm is 3D monolithic integration, a revolutionary approach that achieves unprecedented logic density through Complementary FETs (CFETs), where pMOS and nMOS transistors are vertically stacked, and a dramatic expansion of on-chip memory capacity by integrating memory layers atop logic transistors. The second paradigm leverages the transformative potential of operating chips at cryogenic temperatures where transistors exhibit enhanced performance, and parasitic resistances are substantially minimized. These advancements hold the promise of redefining computing efficiency and performance for the AI era. © 2025 EDAA.

关键词： BiCMOS technology

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Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS to Cryogenic Computing

Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mahdi Benkhelifa Shivendra S. Parihar Anirban Kar Girish Pahwa Yogesh S. Chauhan Hussam Amrouch 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 Department of Electrical Engineering and Computer Sciences University of California Berkeley USA National Yang Ming Chiao Tung University International College of Semiconductor Technology Taiwan Indian Institute of Technology Kanpur India

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

关键词： Semiconductor device modeling Three-dimensional displays High performance computing Random access memory Cryogenics Silicon system-on-chip Logic Three-dimensional integrated circuits Optimization

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Quality of Task Perception based Performance Optimization of Time-delayed Teleoperation

Quality of Task Perception based Performance Optimization of...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Siwen Liu Xiao Xu Zican Wang Dong Yang Zhi Jin Eckehard Steinbach Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology (LMT) and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany Shenzhen Campus of Sun Yat-sen University and Sun Yat-Sen University China

This paper proposes a Quality-of-Task-Perception (QoTP) based performance optimization approach for bilateral haptic teleoperation. For time-delayed teleoperation, stabilizing control schemes are combined with communication and data reduction algorithms to ensure stability, transparency, and Quality of Experience (QoE). An adaptive control scheme switching strategy to improve the QoE of teleoperation considering network quality of service (QoS) and quality of control (QoC) is proposed in our previous work. In this paper, we introduce a novel concept named quality of task perception (QoTP) to optimize teleoperation from another dimension in addition to QoS and QoC. QoTP represents the pre-cognition of the task and the accuracy of the environment restoration. The proposed optimization approach is applied to a haptic teleoperation system with switchable control schemes (prediction-based or passivity-based). An environment restoration model is set on the leader side using the least squares method (LSM) to fit different environment models and provide force feedback without the influence of round-trip delay. We also evaluate the system performance with different delays, control schemes, and model complexities both objectively and subjectively. Our experiments validate the proposed approach and show that the QoE performance increases when selecting the more accurate environment restoration model in the QoTP dimension considering the system’s computing power.

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Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study Assembling a Human Hand Injury Protection Database

arXiv

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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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