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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Hassen Nigatu Li Jihao Keqi Zhu Junhan Zhang Haotian Guo Guodong Lu Doik Kim Robot Perception and Grasp Lab School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Research Center of Yuyao Yuyao Technology Innovation Center Robotics Institute of Zhejiang University Ningbo Shi Zhejiang Province China The State Key Laboratory of Fluid Power and Mechatronic Systems and the Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering School of Mechanical Engineering Zhejiang University Hangzhou China xR Lab Center for Intelligent and Interactive Robotics Artificial Intelligence and Robot Institute Korea Institute of Science and Technology (KIST) South Korea

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3- PRS Parallel Kinematics machines, which are among the most extensively researched and viably successful manipulators for precision machining applications. Despite the wealth of research on these robotic manipulators, no previous work has demonstrated their stiffness performance within the parasitic motion space. Such an undesired motion influences their stiffness properties, as stiffness is configuration-dependent. Addressing this gap, this paper develops a stiffness model that accounts for both the velocity-level parasitic motion space and the regular workspace. Numerical simulations are provided to illustrate the stiffness characteristics of the manipulator across all considered spaces. The results indicate that the stiffness profile within the parasitic motion space is both shallower and the values are smaller when compared to the stiffness distribution across the orientation workspace. This implies that evaluating a manipulator's performance adequately requires assessing its ability to resist external loads during parasitic motion. Therefore, comprehending this aspect is crucial for redesigning components to enhance overall stiffness.

关键词： Simulation Resists Machining Kinematics Manipulators Numerical simulation Magnetic heads

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6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping

arXiv

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arXiv 2022年

作者： Cao, Hongpeng Dirnberger, Lukas Bernardini, Daniele Piazza, Cristina Caccamo, Marco School of Engineering and Design Technical University of Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation. 6IMPOSE consists of four modules: First, a data generation pipeline that employs the 3D software suite Blender to create synthetic RGBD image datasets with 6D pose annotations. Second, an annotated RGBD dataset of five household objects generated using the proposed pipeline. Third, a real-time two-stage 6D pose estimation approach that integrates the object detector YOLO-V4 and a streamlined, real-time version of the 6D pose estimation algorithm PVN3D optimized for time-sensitive robotics applications. Fourth, a codebase designed to facilitate the integration of the vision system into a robotic grasping experiment. Our approach demonstrates the efficient generation of large amounts of photo-realistic RGBD images and the successful transfer of the trained inference model to robotic grasping experiments, achieving an overall success rate of 87% in grasping five different household objects from cluttered backgrounds under varying lighting conditions. This is made possible by the fine-tuning of data generation and domain randomization techniques, and the optimization of the inference pipeline, overcoming the generalization and performance shortcomings of the original PVN3D algorithm. Finally, we make the code, synthetic dataset, and all the pretrained models available on Github. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

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MotionTrack: Learning Robust Short-Term and Long-Term Motions for Multi-Object Tracking

MotionTrack: Learning Robust Short-Term and Long-Term Motion...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zheng Qin Sanping Zhou Le Wang Jinghai Duan Gang Hua Wei Tang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University School of Software Engineering Xi'an Jiaotong University Wormpex AI Research University of Illinois at Chicago

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features to re-identify the lost targets after a long period. However, the reliability of motion prediction and the discriminability of appearances can be easily hurt by dense crowds and extreme occlusions in the tracking process. In this paper, we propose a simple yet effective multi-object tracker, i.e., MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection. Our Interaction Module and Refind Module are embedded in the well-known tracking-by-detection paradigm, which can work in tandem to maintain superior performance. Extensive experimental results on MOT17 and MOT20 datasets demonstrate the superiority of our approach in challenging scenarios, and it achieves state-of-the-art performances at various MOT metrics. Code is available at https://***/qwomeng/MotionTrack.

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Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation

arXiv

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arXiv 2023年

作者： Huang, Ziyue Dang, Lujuan Xie, Yuqing Ma, Wentao Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong a University Xi’an710049 China The School of Electrical Engineering Xi’an University of Technology Xi’an710048 China

State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries. Due to issues t such as varying data distribution and sequence length in different cycles, most existing methods require health feature extraction technique, which can be time-consuming and labor-intensive. c GRU can well solve this problem due to the simple structure and superior performance, receiving widespread attentions. However, redundant information still exists within the network and impacts the accuracy of SOH estimation. To address this issue, a new GRU network based on Hilbert-Schmidt Independence Criterion (GRU-HSIC) is proposed. First, a zero masking network is used to transform all battery data measured with varying lengths every cycle into sequences of the same length, while still retaining information about the original data size in each cycle. Second, the Hilbert-Schmidt Independence Criterion (HSIC) [ bottleneck, which evolved from Information Bottleneck (IB) t theory, is extended to GRU to compress the information from hidden layers. To evaluate the proposed method, we conducted experiments on datasets from the Center for Advanced Life Cycle t engineering (CALCE) of the University of Maryland and NASA Ames Prognostics Center of Excellence. Experimental results demonstrate that our model achieves higher accuracy than other recurrent models. Copyright © 2023, The Authors. All rights reserved.

关键词： Life cycle

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Frontiers in AI Acceleration: From Approximate Computing to FeFET Monolithic 3D Integration

Frontiers in AI Acceleration: From Approximate Computing to ...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Shubham Kumar Paul R. Genssler Somaya Mansour Yogesh Singh Chauhan Hussam Amrouch Semiconductor Test and Reliability (STAR) University of Stuttgart Stuttgart Germany Department of Electrical Engineering Indian Institute of Technology Kanpur India Chair of AI Processor Design Technical University of Munich (TUM) Germany TUM Munich Institute of Robotics and Machine Intelligence Germany

With the rapidly expanding applications of artificial intelligence (AI), the quest for hardware acceleration to foster high-speed and energy-efficient AI computation has become ever more important. In this work, we first explore the performance and energy advantages of employing classical AI acceleration with conventional systolic multiply-accumulate (MAC) arrays. We then highlight the growing importance of monolithic 3D integration as a transformative hardware acceleration strategy, moving beyond the constraints of classical von Neumann architectures. We also discuss how brain-inspired hyperdimensional computing (HDC) offers an exciting avenue for overcoming the power-hungry requirements often associated with MAC arrays, which are inevitable in deep learning hardware. Addressing the limitations of von Neumann architectures, we present the potential of monolithic 3D integration to enable ultra-dense Processing-in-Memory (PiM) layers stacked on top of high-performance CMOS logic. This novel approach offers to enhance computational performance. Recognizing the need for compatibility with low thermal budgets, we identify ferroelectric thin-film transistors (FeTFT) as a promising candidate for back-end-ofline (BEOL) fabrication. We highlight recent advances in BEOL FeTFT technology and demonstrate how technology/algorithm co-optimization plays a crucial role in the successful realization of reliable brain-inspired HDC on potentially unreliable FeTFT-based PiM layers. Our results showcase the potential of these innovations for the development of next-generation, energy-efficient AI hardware.

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pourin...

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IEEE International Conference on Communications (ICC)

作者： Edwin Babaians Praveen Gorla Serkut Ayvaşık Jan Plachý Zdenek Becvar Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University Prague Chair of Communication Networks School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

关键词： 5G mobile communication Urban areas Medical services Streaming media User experience Haptic interfaces Task analysis

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

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

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International Conference on Dependable Systems and Their Applications (DSA)

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

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.

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Invited Paper: Ultra-Efficient Edge AI Using FeFET-based Monolithic 3D Integration

Invited Paper: Ultra-Efficient Edge AI Using FeFET-based Mon...

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IEEE International Conference on Computer-Aided design

作者： Shubham Kumar Yogesh Singh Chauhan Hussam Amrouch Semiconductor Test and Reliability (STAR) University of Stuttgart Stuttgart Germany Department of Electrical Engineering Indian Institute of Technology Kanpur India Chair of AI Processor Design Technical University of Munich (TUM) Germany Munich Institute of Robotics and Machine Intelligence TUM Germany

Monolithic three-dimensional (M3D) integration signifies a notable technological leap by providing solutions of high density and energy efficiency, particularly for ultra-efficient edge AI solutions. Among emerging technologies, ferroelectric thin-film transistors (FeTFTs) have attracted substantial interest due to their potential in neuromorphic computing and their compatibility with back-end-of-the-line (BEOL) fabrication processes. Nevertheless, the challenge of M3D integrated circuits lies in elevated temperatures resulting from limited heat dissipation across various stacked tiers, subsequently exerting adverse effects on system reliability. In this work, we explore how compute-in-memory (CIM) architectures can be realized using BEOL FeTFT to accelerate deep learning applications. We demonstrate how generated temperature impacts the reliability and ultimately degrade the inference accuracy. To achieve that, we have modified the open-source “3D+NeuroSim” framework to introduce FeTFT and then employed it to estimate the key figures of merit, such as throughput, power density, area, etc. Then, we perform a comprehensive thermal analysis for the simulated M3D architecture to explore how the DNN accuracy will be impacted due to the generated heat. We also demonstrate how FeTFT devices can be accurately modeled using TCAD simulations and how run-time variability (due to temperature effects) and design-time variability (due to process variation) impact the reliability of FeTFT transistors.

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A Lightweight Model for Perceptual Image Compression via Implicit Priors

arXiv

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arXiv 2025年

作者： Wei, Hao Zhou, Yanhui Jia, Yiwen Ge, Chenyang Anwar, Saeed Mian, Ajmal National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Information and Telecommunication Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Software Engineering The University of Western Australia Crawley PerthWA6009 Australia

Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented neural methods. However, existing methods typically improve compression performance by incorporating explicit semantic priors, such as segmentation maps and textual features, into the encoder or decoder, which increases model complexity by adding parameters and floating-point operations. This limits the model’s practicality, as image compression often occurs on resource-limited mobile devices. To alleviate this problem, we propose a lightweight perceptual Image Compression method using Implicit Semantic Priors (ICISP). We first develop an enhanced visual state space block that exploits local and global spatial dependencies to reduce redundancy. Since different frequency information contributes unequally to compression, we develop a frequency decomposition modulation block to adaptively preserve or reduce the low-frequency and high-frequency information. We establish the above blocks as the main modules of the encoder-decoder, and to further improve the perceptual quality of the reconstructed images, we develop a semantic-informed discriminator that uses implicit semantic priors from a pretrained DINOv2 encoder. Experiments on popular benchmarks show that our method achieves competitive compression performance and has significantly fewer network parameters and floating point operations than the existing state-of-the-art. We will release the code and trained models. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Cross-Template-Based Hypergraph Transformer

Cross-Template-Based Hypergraph Transformer

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

作者： Jingxi Feng Xiangmin Han Heming Xu Juan Wang Jue Jiang Shaoyi Du Yue Gao National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Software Tsinghua University Beijing China Department of Ultrasound The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Single-template-based brain functional network analysis methods can provide limited functional connectivity information, which constrains the performance of brain disease diagnosis. Previous works have explored multi-template functional network analysis but failed to integrate the high-order correlation information within templates and the complementary information between templates into a unified relationship strength between nodes, and we extract the high-order correlation information within each template through hypergraph convolution. Secondly, for the analysis of functional connectivity between templates, we propose a cross-template Transformer to capture long-range dependencies between templates. A cross-template mask is applied to focus the model’s attention on important connections between templates, thereby enhancing model robustness. Finally, we progressively fuse the high-order information captured within templates with the global information across templates for downstream classification tasks. The proposed method has been validated on the public ABIDE dataset, and it outperforms existing methods in the ASD diagnosis task.

关键词： Correlation Fuses Convolution Network analyzers Transformers Robustness Acoustics Medical diagnosis Data mining Speech processing

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