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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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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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Point Cloud Segmentation Network Based on Multi-Scale Feature Fusion

Point Cloud Segmentation Network Based on Multi-Scale Featur...

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Chinese Automation Congress (CAC)

作者： Hao Deng Peng Cheng Shengmei Cheng Cheng Liu Shaoyi Du Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Service School of Information Science and Technology Northwest University Xi'an China 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 Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at improving the local feature extraction for point cloud learning, we introduce a point cloud segmentation network that enhances the PointNet++ framework with a local feature transformation module and a multi-scale feature fusion module. The local feature transformation module employs operations akin to convolution to restructure the point cloud's local feature dimensions, thus boosting the network's ability to extract detailed local features. Additionally, the multi-scale feature fusion module integrates semantic features of point clouds across various scales using a layered network design, aiming to heighten segmentation precision. Experimental results indicate that the proposed network can achieve a robust point cloud segmentation accuracy, reaching an accuracy rate of 85.8%.

关键词： Point cloud compression Accuracy Automation Convolution Semantics Feature extraction Boosting

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Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network

Haptic Dataset Augmentation with Subjective QoE Labels using...

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

作者： Zican Wang Xiao Xu Dong Yang Zhenyu Wang Sarah Shtaierman 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) Centre for Tactile Internet with Human-in-the-Loop (CeTI) TU Dresden

This paper proposes a novel Generative Adversarial Network (GAN)-based strategy to augment subjective haptic Quality of Experience (QoE) datasets for bilateral teleoperation with haptic feedback without conducting time-consuming subjective experiments. In our previous work, we proposed a multi-assessment fusion approach to predict subjective haptic quality using a collection of objective metrics. This method requires a sufficiently large haptic dataset with QoE labels. The proposed generative approach automatically expands the existing haptic quality dataset by combining a modified conditional GAN (CGAN) and Style GAN (StyleGAN) architecture. The most important feature of our method is that it learns from the labeled training data and focuses on synthesizing signals with artifacts according to new input labels containing the QoE score, time delay, control method, and data reduction information. Extensive experiments are conducted to validate the suitability of the expanded dataset. The results show that our approach is able to generate new data, which match the label and signal distribution of the original data with categorical rank and linear correlation of over 0.85.

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Safety-Critical Scenario Generation by Causal Influence Detection

Safety-Critical Scenario Generation by Causal Influence Dete...

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International Conference on Intelligent Transportation

作者： Yibing Yang Chi Zhang Linhai Xu Shuangxun Ma Li Li 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 Xi'an Shaanxi China School of Future Transportation Chang'an University Xi'an China Department of Automation BNRist Tsinghua University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Appropriate behavior intervention for a specific background vehicle at the right moment is a crucial step in identifying safety-critical scenarios. Existing methods generate scenarios by estimating the data distribution, but when noise interventions are present, the emergence of safety-critical scenarios becomes colinearity with the noise interventions, potentially resulting in unnecessary interventions and unrealistic vehicle behaviors. To address this, we propose to integrate the causal relationship between the background vehicle behavior and the safety status of ego-vehicle as a prior into scenario generation. In this notion, we present a reinforcement learning framework with a causal influence detection module (CausalID). Specifically, we employ conditional mutual information to quantify the causal influence between vehicle sequential behaviors, and intervene on the background vehicle's behavior when the causal influence exceeds a certain threshold. Additionally, to prevent unrealistic collision-oriented driving, we sample the final intervention action from a set of candidate actions according to their probabilities of occurring in natural driving scenarios. Our experiments in 3-lane highway scenarios with multiple vehicles validate the effectiveness of the proposed framework.

关键词： Road transportation Measurement Noise Reinforcement learning Safety Complexity theory Scenario generation Mutual information Intelligent transportation systems Multi-agent systems

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Functional Mode Switching for Safe and Efficient Human-Robot Interaction

Functional Mode Switching for Safe and Efficient Human-Robot...

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IEEE-RAS International Conference on Humanoid Robots

作者： Petr Svarny Mazin Hamad Alexander Kurdas Matej Hoffmann Saeed Abdolshah Sami Haddadin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Engineering Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technische Universität München (TUM)

ISBN: (纸本)9798350309805

Various approaches can ascertain the safety and efficiency of physical human-robot collaboration. This paper presents the concept of robot functional mode switching to efficiently ensure human safety during collaborative tasks based on biomechanical pain and injury data and task information. Besides the robot's reflected inertial properties summarizing its impact dynamics, our concept also integrates safe and smooth velocity shaping that respects human partner motion, interaction type, and task knowledge. We further discuss different approaches to safely shape the robot velocity without sacrificing the overall task execution time and motion smoothness. The experimental results showed that our proposed approaches could decrease jerk level during functional mode switching and limit the impact of safety measures on productivity, especially when guided with additional task knowledge.

关键词： Robot motion Service robots Human-robot interaction Humanoid robots Switches Interference Safety

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HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration

arXiv

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arXiv 2023年

作者： Tang, Canhui Li, Yiheng Du, Shaoyi Wang, Guofa Tian, Zhiqiang 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 Xi’an China Shunan Academy of Artificial Intelligence Zhejiang Ningbo315000 China School of Software Engineering Xi’an Jiaotong University Xi’an China

Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-level descriptors and detectors and performs coarse-to-fine matching with them. First, to explicitly learn local and global semantics, we propose a hierarchical contrastive learning strategy, training the robust matching ability of high-level descriptors, and refining the local feature space using low-level descriptors. Furthermore, we propose to learn dual-level saliency maps that extract two groups of keypoints in two different senses. To overcome the weak supervision of binary matchability labels, we propose a ranking strategy to label the significance ranking of keypoints, and thus provide more fine-grained supervision signals. Finally, we propose a global-to-local matching scheme to obtain robust and accurate correspondences by leveraging the complementary dual-level features. Quantitative experiments on 3DMatch and KITTI odometry datasets show that our method achieves robust and accurate point cloud registration and outperforms recent keypoint-based methods. [code release] Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration

HD2Reg: Hierarchical Descriptors and Detectors for Point Clo...

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IEEE Symposium on Intelligent Vehicle

作者： Canhui Tang Yiheng Li Shaoyi Du Guofa Wang Zhiqiang Tian National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an Jiaotong University Xi'an China Shunan Academy of Artificial Intelligence Ningbo P.R. China School of Software Engineering Xi'an Jiaotong University Xi'an China

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Risk Estimation for ICU Patients with Personalized Anomaly-Encoded Bedside Patient Data

Risk Estimation for ICU Patients with Personalized Anomaly-E...

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

作者： Kai Wu Ee Heng Chen Felix Wirth Keti Vitanova Rüdiger Lange Darius Burschka German Heart Center Munich Munich Germany Department of Computer Engineering Machine Vision and Perception Group TUM School of Computation Information and Technology Technical University of Munich Garching Germany MIRMI - Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich

We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.

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Object-Centric Grasping Transferability: Linking Meshes to Postures

Object-Centric Grasping Transferability: Linking Meshes to P...

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IEEE-RAS International Conference on Humanoid Robots

作者： Diego Hidalgo-Carvajal Carlos Magno C. O. Valle Abdeldjallil Naceri Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence (RSI) TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet with Human-in-the-Loop (CeTI)

ISBN: (纸本)9798350309805

Attaining human hand manipulation capabilities is a sought-after goal of robotic manipulation. Several works have focused on understanding and applying human manipulation insights in robotic applications. However, few considered objects as central pieces to increase the generalization properties of existing methods. In this study, we explore context-based grasping information transferability between objects by using mesh-based object representations. To do so, we empirically labeled, in a mesh point-wise manner, 10 grasping postures onto a set of 12 purposely selected objects. Subsequently, we trained our convolutional neural network (CNN) based architecture with the mesh representation of a single object, associating grasping postures to its local regions. We tested our network across multiple objects of distinct similarity values. Results show that our network can successfully estimate non-feasible grasping regions as well as feasible grasping postures. Our results suggest the existence of an abstract relation between the predicted context-based grasping postures and the geometrical properties of both the training and test objects. Our proposed approach aims to expand grasp learning research by linking local segmented meshes to postures. Such a concept can be applied to grasp new objects using anthropomorphic robot hands.

关键词： Training Humanoid robots Grasping Convolutional neural networks

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