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    Q&A: Helping robots identify objects in cluttered spaces

    1-2页
    查看更多>>摘要：Imagine a coffee cup sitting on a table. Now, imagine a book partially obscuring the cup. As humans, we still know what the coffee cup is even though we can’t see all of it. But a robot might be confused. Robots in warehouses and even around our houses struggle to identify and pick up objects if they are too close together, or if a space is cluttered. This is because robots lack what psychologists call “object unity,” or our ability to identify things even when we can’t see all of them. Researchers at the University of Washington have developed a way to teach robots this skill. The method, called THOR for short, allowed a low-cost robot to identify objects - including a mustard bottle, a Pringles can and a tennis ball - on a cluttered shelf. In a recent paper published in IEEE Transactions on Robotics, the team demonstrated that THOR outperformed current state-of-the-art models.
      原文链接:
    • NETL
    • NSTL

    Findings from University College Cork Advance Knowledge in Robotics (Design and Modeling of a Non-Flat Foldable Tubular Kirigami With Compliant Joints)

    2-3页
    查看更多>>摘要：Researchers detail new data in robotics. According to news reporting originating from Cork, Ireland, by NewsRx correspondents, research stated, “This paper applies the kirigami technique to a non-rigid foldable tubular origami to make a rigid foldable tubular design, i.e., a radially closable kirigami (RC-kiri).” Financial supporters for this research include China Scholarship Council. Our news journalists obtained a quote from the research from University College Cork: “The laminar emergent torsional (LET) compliant joint is applied to surrogate the crease, which makes the design applicable in practical engineering applications. By incorporating a non-flat folding design, the folding angles of each crease are minimized, leading to a reduction in the strain exerted on engineering materials. The kinetostatic theoretical model is constructed using the principle of virtual work, and its results are compared with those obtained from a simulation model in finite element analysis (FEA). A 3D printed physical model is tested to obtain the relationship between forces and displacements.”
      原文链接:
    • NETL
    • NSTL

    VSB-Technical University of Ostrava Reports Findings in Bacterial Infections and Mycoses (Chemical multiscale robotics for bacterial biofilm treatment)

    3-4页
    查看更多>>摘要：New research on Bacterial Infections and Mycoses is the subject of a report. According to news reporting originating from Ostrava, Czech Republic, by NewsRx correspondents, research stated, “A biofilm constitutes a bacterial community encased in a sticky matrix of extracellular polymeric substances. These intricate microbial communities adhere to various host surfaces such as hard and soft tissues as well as indwelling medical devices.” Financial support for this research came from European Regional Development Fund. Our news editors obtained a quote from the research from the VSB-Technical University of Ostrava, “These microbial aggregates form a robust matrix of extracellular polymeric substances (EPSs), leading to the majority of human infections. Such infections tend to exhibit high resistance to treatment, often progressing into chronic states. The matrix of EPS protects bacteria from a hostile environment and prevents the penetration of antibacterial agents. Modern robots at nano, micro, and millimeter scales are highly attractive candidates for biomedical applications due to their diverse functionalities, such as navigating in confined spaces and targeted multitasking. In this tutorial review, we describe key milestones in the strategies developed for the removal and eradication of biofilms using robots of different sizes and shapes.”
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    New Androids Data Have Been Reported by Investigators at Biomedical University of Rome (Artificial Emotions: Toward a Human-centric Ethics)

    4-5页
    查看更多>>摘要：A new study on Robotics - Androids is now available. According to news reporting originating from Rome, Italy, by NewsRx correspondents, research stated, “One of the most challenging goals in social robotics is implementing emotional skills. Making robots capable of expressing and deciphering emotions is considered crucial for allowing humans to socially interact with them.” Our news editors obtained a quote from the research from the Biomedical University of Rome, “In addition to presenting technical challenges, the implementation of artificial emotions in artificial systems raises intriguing ethical issues. In this paper, moving from the case study of a human android, we present a relational perspective on human-robot interaction, claiming that, since robots are material objects not endowed with subjectivity, only an asymmetrical relationship can be established between robots and humans. Based on this claim, we deal with some of the most relevant issues in roboethics, such as transparency, trust, and authenticity.” According to the news editors, the research concluded: “We conclude suggesting that a machinecentered approach to ethics should be abandoned in favor of a relational approach, which revalues the centrality of human being in the Human-Robot Interaction.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Technical University Munich (TU Munich) Reports Findings in Machine Learning (Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance)

    5-6页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Munich, Germany, by NewsRx editors, research stated, “Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions.” Our news journalists obtained a quote from the research from Technical University Munich (TU Munich), “Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability. This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r = 0.76, RMSE = 3.13 %, validation r = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters. This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible.”
      原文链接:
    • NETL
    • NSTL

    Study Results from Leibniz University Hannover in the Area of Robotics Reported (Predictive Multi-agent-based Planning and Landing Controller for Reactive Dual-arm Manipulation)

    6-7页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting originating from Hannover, Germany, by NewsRx correspondents, research stated, “Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans.” Financial support for this research came from State of Bavaria for the Geriatronics project. Our news editors obtained a quote from the research from Leibniz University Hannover, “This work addresses the issues with vector-field-based motion generation strategies, which are often prone to localminima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introduced cooperative dual task space and flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators from Ministry of Water Resources Report New Data on Machine Learning (A Method To Predict the Peak Shear Strength of Rock Joints Based On Machine Learning)

    7-8页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In geotechnical and tunneling engineering, accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments. Peak shear strength (PSS), being the paramount mechanical property of joints, has been a focal point in the research field.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Open Fund of Anhui Province Key Laboratory of Building Structure and Underground Engineering, Anhui Jianzhu University, Open Research Fund of Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research.
      原文链接:
    • NETL
    • NSTL
    • Science Press

    Data on Machine Learning Reported by Jean-Paul Charbonnier and Colleagues (Estimating lung function from computed tomography at the patient and lobe level using machine learning)

    8-9页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Nijmegen, Netherlands, by NewsRx correspondents, research stated, “Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.” Our news editors obtained a quote from the research, “To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient’s lung function. We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient’s total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe-level and a patient-level model. The lobe-level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient-level model directly estimates patient level lung function from a CT scan and is used to re-scale the output of the lobe-level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8 433 CT volumes for training, 1 775 CT volumes for validation, and 1 873 CT volumes for testing. First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe-level quantities, such as COVID-19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient-level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono-oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377 L, 0.297 L, and 2.800 mL/min/mm Hg respectively. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient’s lung function.”
      原文链接:
    • NETL
    • NSTL

    Findings from Reva University Provides New Data about Machine Learning (Enhancing Temple Surveillance Through Human Activity Recognition: a Novel Dataset and Yolov4-convlstm Approach)

    9-10页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating in Karnataka, India, by NewsRx journalists, research stated, “Automated identification of human activities remains a complex endeavor, particularly in unique settings like temple environments. This study focuses on employing machine learning and deep learning techniques to analyze human activities for intelligent temple surveillance.” The news reporters obtained a quote from the research from Reva University, “However, due to the scarcity of standardized datasets tailored for temple surveillance, there is a need for specialized data. In response, this research introduces a pioneering dataset featuring Eight distinct classes of human activities, predominantly centered on hand gestures and body postures. To identify the most effective solution for Human Activity Recognition (HAR), a comprehensive ablation study is conducted, involving a variety of conventional machine learning and deep learning models. By integrating YOLOv4’s robust object detection capabilities with ConvLSTM’s ability to model both spatial and temporal dependencies in spatio-temporal data, the approach becomes capable of recognizing and understanding human activities in sequences of images or video frames. Notably, the proposed YOLOv4-ConvLSTM approach emerges as the optimal choice, showcasing a remarkable accuracy of 93.68%.”
      原文链接:
    • NETL
    • NSTL
    • Ios Press

    New Robotics Study Findings Have Been Reported by Researchers at Beijing University of Posts and Telecommunications (Color Filterbased Gait Silhouette Extraction Method In Dynamic Background)

    10-11页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The smart walker is a special service robot that can assist the elderly in walking. Deploying gait recognition algorithms on the smart walker can help the elderly identify relatives and friends from a distance in real time and obtain some pedestrian information to avoid danger.” Financial support for this research came from The authors have no conflict of interest to declare. All human data are from public datasets.. Our news journalists obtained a quote from the research from the Beijing University of Posts and Telecommunications, “However, in actual usage scenarios, it is difficult to quickly obtain qualified pedestrian silhouettes as input images for gait recognition algorithms. The difficulty is that unlike fixed cameras in traditional gait recognition scenarios, the camera of a smart walker acquires a dynamic background due to the movement of the robot, and it is difficult to accurately segment dynamic pedestrians from the dynamic background. Instance segmentation algorithms commonly used to segment silhouettes in static images require high-resolution input images. To quickly segment gait silhouettes from dynamic backgrounds, we propose a color filter-based gait silhouette extraction method (CFSE) in dynamic background. We use instance segmentation algorithms to distinguish the foreground and background, use background subtraction algorithms to obtain portrait silhouettes, and finally use color filtering to eliminate background colors to get the silhouette.”
      原文链接:
    • NETL
    • NSTL
    • Spie-Soc Photo-Optical Instrumentation Engineers