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IEEE-RAS International Conference on Humanoid Robots

作者： Tyler Becker Song Jiang David Feil-Seifer Monica Nicolescu Department of Computer Science & Engineering Robotics Research lab University of Nevada Reno Reno NV USA Department of Computer Science & Engineering Socially Assistive Robotics Group University of Nevada Reno Reno NV USA

This paper addresses the problem of dynamic allocation of robot resources to tasks with hierarchical representations and multiple types of execution constraints, with the goal of enabling single-robot multitasking capabilities. Although the vast majority of robot platforms are equipped with more than one sensor (cameras, lasers, sonars) and several actuators (wheels/legs, two arms), which would in principle allow the robot to concurrently work on multiple tasks, existing methods are limited to allocating robots in their entirety to only one task at a time. This approach employs only a subset of a robot's sensors and actuators, leaving other robot resources unused. Our aim is to enable a robot to make full use of its capabilities by having an individual robot multitask, distributing its sensors and actuators to multiple concurrent activities. We propose a new architectural framework based on Hierarchical Task Trees that supports multitasking through a new representation of robot behaviors that explicitly encodes the robot resources (sensors and actuators) and the environmental conditions needed for execution. This architecture was validated on a two-arm, mobile, PR2 humanoid robot, performing tasks with multiple types of execution constraints.

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Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

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computers, Materials & Continua 2022年第12期73卷 4465-4487页

作者： Zia Ullah Muhammad Ismail Mohmand Sadaqat ur Rehman Muhammad Zubair Maha Driss Wadii Boulila Rayan Sheikh Ibrahim Alwawi Department of Computer Science The Brains InstitutePeshawar25000Pakistan School of Natural and Computing Sciences University of AberdeenAberdeenUK Department of Neurosciences KU Leuven Medical SchoolLeuven3000Belgium Security Engineering Laboratory CCISPrince Sultan UniversityRiyadh12435Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science Robert Gordon UniversityAberdeenUK

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it *** overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature ***,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature ***,the three feature extraction models are utilized using Ensemble Learning techniques for final expression *** representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising *** evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression *** proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and ***,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.

关键词： Ensemble learning emotion recognition feature fusion occlusion

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Empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder 5th

Empirical Study of Computational Intelligence Approaches for...

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5th International Conference on Intelligent Computing and Applications, ICICA 2019

作者： Khatun, Mst. Arifa Ali, Md. Asraf Ahmed, Md. Razu Noori, Sheak Rashed Haider Sahayadhas, Arun Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Artificial Intelligence Research Lab Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9789811555657

The objective of the research is to develop a predictive model that can significantly enhance the detection and monitoring performance of Autism Spectrum Disorder (ASD) using four supervised learning techniques. In this study, we applied four supervised-based classification techniques to the clinical ASD data obtained from 704 patients. Then, we compared the four machine learning (ML) algorithms performance across tenfold cross-validation, ROC curve, classification accuracy, F1 measure, precision, recall, and specificity. The analysis findings indicate that Support Vector Machine (SVM) achieved the uppermost performance than the other classifiers in terms of accuracy (85%), f1 measure (87%), precision (87%), and recall (88%). Our work presents a significant predictive model for ASD that can effectively help the ASD patients and medical practitioners. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Interference cancellation in RIS-assisted FD relay transmission

Interference cancellation in RIS-assisted FD relay transmiss...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Donghyeon Kim Wooyeol Choi Chanjun Chun Yohan Kim Yonggang Kim AI & Robotics Tech Lab (ART Lab) Department of Computer Engineering Chosun University Department of Software Dongseo University Division of Computer Science and Engineering Kongju National University

Reconfigurable intelligent surface (RIS) is one of the promising technology for the next-generation wireless networks. The RIS reflects the received signal with phase shift introduced by reflecting elements without analog to digital conversion. In relay transmission scenario, RIS could be exploited to enhance the network coverage and improve transmission performance. In this paper, we consider RIS-assisted full-duplex (FD) relay transmission scenario. In the considered scenario, RIS and FD relay node performs relay transmission. Although the beamformed signal could be generated by phase shift of RIS, the interference from RIS to FD relay node may degrade the throughput performance. The proposed method cancels both of interference from RIS and self-interference at the FD relay node by estimating interference channels in the preparation phase before data transmission.

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Fairness-Sensitive Policy-Gradient Reinforcement Learning fo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jie Zhu Mengsha Hu Amy Zhang Ruoming Jin Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science George Washington University Department of Computer Science Kent State University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robots assist humans in various activities, from daily living to collaborative manufacturing. Because they have biased learning sources (e.g., data, demonstrations, human feedback), robots inevitably have discriminatory performance regarding individual differences (e.g., skin color, mobility, appearance); discriminatory performance will undermine robots’ service quality, causes request ignorance and response delay, and even cause emotional offenses. Therefore, mitigating biases is critically important for delivering fair robotic services. In this paper, we design a bias-mitigation method – Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots self-identify and correct biased behaviors. FSP-GRL identifies bias by examining the abnormal updates along particular gradients and updates the policy network to provide fair decisions. To validate FSPGRL’s effectiveness, we designed a human-centered service scenario: a robot serving people in a restaurant. With a user study involving 24 humans and 1,000 service demonstrations, FSPGRL has proven effective in maintaining fairness during robot services.

关键词： Image color analysis Design methodology Collaboration Reinforcement learning Skin Manufacturing Delays Robots

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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

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computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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Solving the Farmer's Dilemma with FlexBE Using GR(1) Synthesis

Solving the Farmer's Dilemma with FlexBE Using GR(1) Synthes...

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IEEE Southeastcon

作者： Josh Luzier David C. Conner Department of Physics Computer Science and Engineering Capable Humanitarian Robotics and Intelligent Systems Lab (CHRISLab) Christopher Newport University Newport News Virginia

This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically precise specifications to generate “correct-by-construction” behavior controllers. Our approach builds upon work using the GR(1) (General Reactivity of rank 1) fragment of LTL to synthesize a reactive hierarchical finite state machine that can be directly executed in FlexBE. The presented work expands on previous ROS 1 tools, and extends them to ROS 2 in a way that supports more general specifications that incorporate environmental states. This paper presents an accessible open-source demonstration that serves as a general introduction to these powerful synthesis techniques.

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PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motio...

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

作者： Zhipeng Zhao Bowen Li Yi Du Taimeng Fu Chen Wang Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo Buffalo NY USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. It outperforms existing methods both in accuracy and efficiency and demonstrates data-efficient learning and generalization ability in long-term prediction.

关键词： Bridges Uncertainty Accuracy Neural networks Merging Predictive models Mathematical models Dynamical systems Intelligent robots

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Excavation pits—progress estimation and cause of delay identification

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Construction robotics 2023年第1期7卷 53-63页

作者： Vierling, Axel Groll, Tobias Meckel, Dennis Heim, Kristina Walter, Daniel Körkemeyer, Dr. Karsten Berns, Dr. Karsten Department of Computer Science Robotics Research Lab University of Kaiserslautern-Landau Kaiserslautern Germany Department of Civil Engineering Chair in Construction Management University of Kaiserslautern-Landau Kaiserslautern Germany

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web application called “DiBa” to notice a potential delay. If a measurement is smaller or larger than expected a fault identification is done in the DiBa.

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