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Pan American Health Care Exchanges, PAHCE

作者： L. R. Montero J. A. Bastian A. I. P. SanPablo Universidad La Salle Mexico City Mexico Groups of Applied Intelligent Systems and Collaborative Assistance Robotics Human Motion Analysis and Rehabilitation Engineering Lab National Institute of Rehabilitation Luis Guillermo Ibarra Ibarra Mexico City Mexico

Parkinson’s disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable information to assess the quality of movement in patients with PD [2]. The present work proposes improving the follow-up and support systems issued by specialists in patients with PD by identifying and adequately classifying the activities of daily living (ADL) carried out by these patients by applying programmed artificial neuronal networks (ANN) in Python language. The proposed supervised learning method allowed the classification of 6 ADLs through 64 signals from inertial sensors that made up the database (dataset) to train a multilayer perceptron (MLP), achieving an accuracy of 99%. Also, by applying dimensionality reduction to dataset, it was possible to reach 80% of accuracy in the classification. These results can potentially help the specialist to reduce costs and achieve a more accurate evaluation, seeking to improve monitoring and quality in the rehabilitation of these patients.

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USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series

USD: Unsupervised Soft Contrastive Learning for Fault Detect...

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

作者： Hong Liu Xiuxiu Qiu Yiming Shi Zelin Zang School of Information and Electrical Engineering Hangzhou City University Hangzhou China Academy of Edge Intelligence Hangzhou City University Hangzhou City University China College of Information Engineering Zhejiang University of Technology Hangzhou China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Centre for Artificial Intelligence and Robotics (CAIR) HKISI-CAS HongKong School of Engineering Westlake University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised fault detection in multivariate time series is critical for maintaining the integrity and efficiency of complex systems, with current methodologies largely focusing on statistical and machine learning techniques. However, these approaches often rest on the assumption that data distributions conform to Gaussian models, overlooking the diversity of patterns that can manifest in both normal and abnormal states, thereby diminishing discriminative performance. Our innovation addresses this limitation by introducing a combination of data augmentation and soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. The data augmentation process enriches the dataset with varied representations of normal states, while soft contrastive learning fine-tunes the model’s sensitivity to the subtle differences between normal and abnormal patterns, enabling it to recognize a broader spectrum of anomalies. This dual strategy significantly boosts the model’s ability to distinguish between normal and abnormal states, leading to a marked improvement in fault detection performance across multiple datasets and settings, thereby setting a new benchmark for unsupervised fault detection in complex systems.

关键词： Technological innovation Sensitivity Fault detection Time series analysis Contrastive learning Signal processing Data augmentation Data models Complex systems Speech processing

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Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Demonstrating Autonomous 3D Path Planning on a Novel Scalabl...

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

作者： Eric Sihite Filip Slezak Ioannis Mandralis Adarsh Salagame Milad Ramezani Arash Kalantari Alireza Ramezani Morteza Gharib The Department of Aerospace Engineering California Institute of Technology Pasadena USA Department of Electrical and Computer Engineering The Silicon Synapse Labs Northeastern University Boston USA The Robotics and Autonomous Systems DATA61 CSIRO Brisbane QLD Australia The Jet Propulsion Laboratory Pasadena USA

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

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Trajectory Optimization for Quadruped Mobile Manipulators that Carry Heavy Payload

Trajectory Optimization for Quadruped Mobile Manipulators th...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Dadiotis Arturo Laurenzi Nikos Tsagarakis Humanoid and Human-Centered Mechatronics Research Line Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (纸本)9798350309805

This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally efficient. At the presence of heavy payload, the approach exhibits reduced leg outstretching (thus increased manipulability) in kinematically demanding motions due to the contribution of payload manipulation in the optimization. The framework's computational efficiency and performance is validated through a number of simulation and experimental studies with the bi-manual quadruped CENTAURO robot carrying on its arms a payload that exceeds 15 % of its mass and traversing non-flat terrain.

关键词： Legged locomotion Computational modeling Dynamics Humanoid robots Computational efficiency Planning Quadrupedal robots

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Design and System Integration of Table Tennis Ball-Collecting Robot

Design and System Integration of Table Tennis Ball-Collectin...

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IEEE/SICE International Symposium on System Integration

作者： Tsutomu Sato Kiyoshi Irie Shinya Takaishi Hiroaki Kawamoto Tsukuba Table Tennis Tech Research Group Tsukuba Ibaraki Japan Future Robotics Technology Center Chiba Institute of Technology Narashino Chiba Japan department of chemistry Graduate school of Science Tohoku University Miyazaki Japan Institute of Systems and Information Engineering Center for Cybernics Research University of Tsukuba Tsukuba Ibaraki Japan

In the world of table tennis, practices often involve the use of numerous balls, making post-practice collection a tedious and time-consuming task. To address this challenge, we present an autonomous robot tailored for table tennis ball collection. This robot features a differential-drive wheeled platform, a unique ball collection mechanism using rotating elastic blades, an on-board RGB camera, and a single board computer for data processing. Balls are detected using a deep learning algorithm, allowing the robot to autonomously navigate the closest ball and collect it. Under a consistently controlled environment, including a limited field size and a set number of balls, experiments were conducted to evaluate the robot's core functions and performance clearly. Experimental results revealed that despite employing a straightforward operational algorithm, the robot demonstrated performance comparable to human-operated remote collection in terms of speed and accuracy. Importantly, this study uniquely focuses not on maximizing efficiency but rather on establishing a baseline for future research in this area, setting a benchmark for future studies. Furthermore, our findings provide the foundation for enhancing ball-collecting robots in table tennis settings.

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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On the Use of Torque Measurement in Centroidal State Estimation

On the Use of Torque Measurement in Centroidal State Estimat...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shahram Khorshidi Ahmad Gazar Nicholas Rotella Maximilien Naveau Ludovic Righetti Maren Bennewitz Majid Khadiv Max-Planck Institute for Intelligent Systems Tübingen Germany Humanoid Robots Lab University of Bonn Germany Agility Robotics Oregon USA LAAS-CNRS Toulouse France Tandon School of Engineering New York University USA

State-of-the-art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot executing different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation.

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Multi-Agent Reinforcement Learning for Distributed Workflow Orchestration at the Tactical Edge

Multi-Agent Reinforcement Learning for Distributed Workflow ...

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MILCOM, Military Communications Conference

作者： Alessandro Amato Alessandro Morelli Mattia Fogli Raffaele Galliera Niranjan Suri Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Florida Institute for Human and Machine Cognition (IHMC) Pensacola FL USA Department of Engineering University of Ferrara Italy US Army DEVCOM Army Research Laboratory (ARL) Adelphi MD USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

The dynamic nature of tactical edge networks has led to the design of architectures that enable real-time data processing and analytics at the edge, to ensure the continuation of operations when the connection to the headquarters is unavailable. However, workflow orchestration faces unique challenges over frequently disconnected, intermittent, and limited (DIL) networks, where traditional approaches, mainly developed for cloud-like environments, lack the flexibility to react promptly to ever-changing conditions. This paper presents a novel decentralized partially observable Markov decision process (DEC-POMDP) formulation for the distributed workflow orchestration problem, where agents need to cooperate to maximize the computation efficiency while reducing the data transmission time. We propose a solution based on multi-agent reinforcement learning (MARL) that leverages graph convolutional reinforcement learning (DGN) and graph attention networks (GAT) to enable agents to share information with each other, capture the network’s structural information, ensure scalability, and eliminate the needs for global knowledge of the network. Training and experiments, which compare our solution with the corresponding constraint satisfaction problem (CSP), are conducted in a simulated 2D urban scenario that mimics nodes’ mobility and communications, showing promising results.

关键词： Training Military communication Knowledge engineering Scalability Markov decision processes MIMICs Reinforcement learning Real-time systems Faces Electronics packaging

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Crossing Domains for AU Coding: Perspectives, Approaches, and Measures

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 158-171页

作者： Ertugrul, Itir Onal Cohn, Jeffrey F. Jeni, Laszlo A. Zhang, Zheng Yin, Lijun Ji, Qiang Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Pittsburgh PittsburghPA15260 United States Robotics Institute Carnegie Mellon University PittsburghPA13902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

Facial action unit (AU) detectors have performed well when trained and tested within the same domain. How well do AU detectors transfer to domains in which they have not been trained? We review literature on cross-domain transfer and conduct experiments to address limitations of prior research. We evaluate generalizability in four publicly available databases. EB+ (an expanded version of BP4D+), Sayette GFT, DISFA and UNBC Shoulder Pain (SP). The databases differ in observational scenarios, context, participant diversity, range of head pose, video resolution, and AU base rates. In most cases performance decreased with change in domain, often to below the threshold needed for behavioral research. However, exceptions were noted. Deep and shallow approaches generally performed similarly and average results were slightly better for deep model compared to shallow one. Occlusion sensitivity maps revealed that local specificity was greater for AU detection within than cross domains. The findings suggest that more varied domains and deep learning approaches may be better suited for generalizability and suggest the need for more attention to characteristics that vary between domains. Until further improvement is realized, caution is warranted when applying AU classifiers from one domain to another. © 2019 IEEE.

关键词： Behavioral research

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3DSGrasp: 3D Shape-Completion for Robotic Grasp

arXiv

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arXiv 2023年

作者： Mohammadi, Seyed S. Duarte, Nuno F. Dimou, Dimitris Wang, Yiming Taiana, Matteo Morerio, Pietro Dehban, Atabak Moreno, Plinio Bernardino, Alexandre Del Bue, Alessio Santos-Victor, José Vislab Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Department of Marine Electrical Electronic and Telecommunications Engineering University of Genoa Italy Genoa Italy Fondazione Bruno Kessler Trento Italy

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance. Copyright © 2023, The Authors. All rights reserved.

关键词： Network layers

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