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Reinforcement Learning for Quadrupedal Locomotion: Current Advancements and future Perspectives

Reinforcement Learning for Quadrupedal Locomotion: Current A...

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Mechanical Engineering and Robotics Research (ICMERR), International conference on

作者： Maurya Gurram Prakash Kumar Uttam Shantipal S. Ohol Computer Science and Engineering Vellore Institute of TechnologyAP Amaravati India Electrical Electronics and Computer Science Indian Institute of Science Bangalore India COEP University Pune India

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments.

关键词： Legged locomotion Training Adaptation models Reinforcement learning Robot sensing systems Robustness Sensors Quadrupedal robots State estimation Optimization

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Exploring Models Approach for Real-Time Hand Gesture Recognition

Exploring Models Approach for Real-Time Hand Gesture Recogni...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International conference on

作者： Nurma' Asih Bimo Sunarfri Hantono Indriana Hidayah Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Real-time hand gesture recognition technology has become an important research area in Human-computer Interaction (HCI) due to its ability to enable more natural and intuitive interactions between humans and digital devices. By relying on hand gestures as input, this technology offers a new way to control devices without physical contact. In recent years, deep learning and computer vision-based approaches have developed rapidly, making significant contributions to improving the accuracy and speed of hand gesture recognition systems. Using the Systematic Literature Review (SLR) method, this study identifies and analyzes research trends, model architectures and algorithm-based approaches, and challenges faced in the development of this technology. This study also analyzes various technical challenges, including such as handling complex backgrounds, varying lighting conditions, and variations in hand shape and size, are also analyzed to illustrate the difficulties in creating a reliable and responsive system. The purpose of this review is to provide a comprehensive overview of current research trends, identify gaps that still need to be filled, and make recommendations for future research and development directions. Through this analysis, it is hoped that this research can help researchers and practitioners understand the existing developments and challenges and encourage innovation for more efficient and effective applications to assist people with disabilities.

关键词： Hands YOLO Human computer interaction Shape Gesture recognition People with disabilities Market research Real-time systems Long short term memory Systematic literature review

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Blockchain for Patient-Centric Solutions Using Distributed Ledger Technology

Blockchain for Patient-Centric Solutions Using Distributed L...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Sandhya G. Vaishnavi T. Subashree M. Naveen Kumar A. Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Blockchain technology, with its decentralized and tamper-resistant nature, has the potential to revolutionize healthcare by improving data security, reliability, and interoperability. By leveraging encryption methods such as Attribute-Based Encryption (ABE), patient records can be securely stored and protected from unauthorized modifications or breaches. Additionally, the integration of smart contracts and standardized data formats enables seamless communication across diverse healthcare systems, fostering a more connected and efficient ecosystem. This patient-focused model enhances data privacy while granting individuals greater control over their medical information. However, the adoption of blockchain in healthcare encounters several challenges, including regulatory hurdles, scalability issues, implementation costs, and acceptance by users. Moreover, integrating blockchain with existing infrastructures and ensuring cooperation among stakeholders adds further complexity. Despite these obstacles, research underscores blockchain's potential to redefine healthcare data management. Addressing these challenges can pave the way for a more secure, interoperable, and patientdriven healthcare system in the future.

关键词： Access control Privacy Data privacy Scalability Data integrity Smart contracts Medical services Blockchains Encryption Stakeholders

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An Innovative Approach to Early Detection of Non-Communicable Diseases Through Machine Learning

An Innovative Approach to Early Detection of Non-Communicabl...

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International conference on ent Computing and control systems (ICICCS)

作者： Karthikeyan K Manoj M Jagadeesh M Sasikumar R Department of Computer Science and Business Systems K.S.Rangasamy College of Technology Namakkal Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Among the advanced machine learning techniques applied in this paper's comprehensive framework for heart disease prediction is the Warm and Naïve Bayes models. The methodology proposed in the paper outperformed traditional methods by 13%, achieving an astounding 88% predicted accuracy using a comprehensive dataset that included vital health variables like age, gender, blood pressure, and cholesterol levels. The work addresses critical issues for the improvement of clinical decision-making and results, like feature selection and model interpretability. Cooperation between data scientists and doctors is also required to ensure smooth integration in clinical workflows as emphasized by the framework. future extensions of this approach could adapt the framework to other non-communicable diseases, thereby broadening its utility for early detection and preventive care. Robust data preprocessing and feature selection techniques are the reason why the models are reliable and adaptable across various patient datasets. Methodological rigor minimizes biases, increases generalizability, and highlights the importance of continuous validation and updates in predictive models for the reflection of emerging health trends and new medical data.

关键词： Heart Adaptation models Accuracy Computational modeling Medical services Predictive models Feature extraction Blood pressure Bayes methods Reliability

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Quality of control Based Resource Dimensioning for Collaborative Edge Robotics

Quality of Control Based Resource Dimensioning for Collabora...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Neelabhro Roy Mani H. Dhullipalla Gourav Prateek Sharma Dimos V. Dimarogonas James Gross School of Electrical Engineering and Computer Science & Digital Futures Research Center KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

关键词： Wireless communication Automation Service robots Robot kinematics System performance Simulation Delays Reliability Robots Multi-agent systems

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Distributed Simulation Evaluation of Digital Communications for Urban Air Mobility (UAM)

Distributed Simulation Evaluation of Digital Communications ...

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Integrated Communications, Navigation and Surveillance conference, ICNS

作者： Terence J. McClain Jason B. Prince Thomas S. White Crew Systems and Aviation Operations Branch NASA Langley Research Center Hampton Virginia USA Crew Systems and Aviation Operations Branch Analytical Mechanics Associates Inc. Hampton Virginia USA

ISBN: (数字)9798331534738

ISBN: (纸本)9798331534745

NASA's Air Mobility Pathfinders (AMP) Project conducted collaborative simulation within a Class Bravo Airspace utilizing the High-Level Architecture (HLA) Standard for Distributed Simulation as the foundation. In this paper, we describe the evaluation of a notional, bi-directional digital communications tool between the UAM Pilot-In-Command (PIC) and legacy Air Traffic control (ATC) that is intended to reduce voice communication over frequency channels. First, we describe the ideation of the digital communications capability. Next, we focus on the software development between the software engineering groups. Then, we discuss input from ATC experts incorporated into the capability. Finally, we outline the interactions between the simulated UAM PICs and ATC using the digital communications capability and some of the key findings for future research.

关键词： Navigation Surveillance Scalability NASA computer architecture Digital communication Standards Frequency control Software engineering Software development management

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Interactive Solutions for Advancing Attention Deficit Hyperactivity Disorder Diagnosis and Management in Children

Interactive Solutions for Advancing Attention Deficit Hypera...

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11th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Walha, Rim Drira, Fadoua Guizani, Imen Regaieg, Wissem Khemakhem, Khaoula Chaabane, Maryam Ayedi, Hela Moalla, Yousr Research Groups in Intelligent Machines REGIM-Lab National Engineering School of Sfax University of Sfax BP 1173 Sfax3038 Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Technological Pole of Sfax BP 242 Sfax3021 Tunisia Child and Family Mental Health Research Laboratory Hedi Chaker University Hospital Center Sfax Tunisia Child Psychiatry Department Hedi Chaker University Hospital Center Sfax Tunisia

ISBN: (纸本)9789897587436

Attention Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental condition that influences a person’s thinking patterns and behavior. Early diagnosis and intervention for ADHD are crucial for improving outcomes, as they can significantly enhance the overall development and well-being of those affected. However, many families encounter various challenges that can lead to delays in treating this disorder. To address these issues, this paper presents an innovative and interactive gaming-based platform designed to enhance the assessment and therapy of ADHD in children. By leveraging engaging gameplay mechanics and user-friendly mobile technology, the proposed solution aims to provide a dynamic platform for evaluating ADHD symptoms while delivering therapy treatment. In particular, our platform incorporates cognitive, behavioral and emotional therapeutic exercises, capable of offering significant support to children with ADHD by enhancing self-control, managing impulse and building organizational skills. These exercises are carefully selected by healthcare professionals. Clinical insights and feedback from child psychologists were integral in developing the games’ mechanics, which not only assess attention but also reinforce coping strategies and behavioral skills. This study underscores the potential of mobile gaming as a transformative tool in pediatric mental health care, paving the way for future research and application in this critical area. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： mHealth

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AI-Driven Traffic Congestion Management: A Predictive Analytics Approach for Smart Cities

AI-Driven Traffic Congestion Management: A Predictive Analyt...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Saurabh Pratap Singh Rathore Yousef Farhaoui Ebuka Emmanuel Aniebonam Tapsi Nagpal Thanuja M Priyanka Kaushik Dept. of Management & Research ICAPSR New Delhi India Dept. of Computer Science Moulay Ismail University Errachidia Morocco Dept. of Business University Marshall MN Dept. of Computer Science & Engineering Lingayas Vidyapeeth Haryana India Sai Vidya Institute of Technology Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Rapid urbanization, increased vehicle usage, and the growth of developing cities have resulted in severe traffic congestion, leading to economic losses, environmental harm, and a decline in quality of life. Traditional traffic management systems tend to be reactive, focusing on addressing traffic flow issues after they occur, rather than proactively managing them. To tackle this challenge, this paper proposes an AI-driven system designed to predict and manage traffic congestion. The system leverages continuous traffic data from IoT devices, such as images, GPS, and inductive loop sensors, to monitor real-time traffic conditions. Traffic predictions are made using a combination of CNN and LSTM networks. Additionally, Geographic Information System (GIS) technology is integrated for spatial analysis and dynamic traffic control, enabling real-time traffic light adjustments and providing drivers with alternative routes to avoid congestion. Preliminary experiments show that intelligent traffic management systems can significantly improve traffic flow and reduce congestion in large cities. The paper also explores challenges related to data privacy, scalability, and future advancements, including the integration of self-driving vehicles and the application of reinforcement learning. This system offers a practical solution for alleviating congestion and optimizing urban transportation systems.

关键词： Smart cities Reinforcement learning Traffic control Real-time systems Sensor systems Internet of Things Vehicle dynamics Traffic congestion Long short term memory Geographic information systems

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AI-based Smart control systems for Induction Brazing in Industrial Applications

AI-based Smart Control Systems for Induction Brazing in Indu...

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Renukhadevi M Rani Patil Deepika Ghanasham Sarode Saloni Abhay Sakharwade Assistant Professor First Year Engineering (Math) Dr. D. Y. Patil Institute of Technology Pimpri Pune Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Tathawade Pune India Assistant Professor Dr. D. Y. Patil Institute of Technology Pimpri Pune Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research Pimpri Pune

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Induction brazing is extensively used in the automotive, aerospace, and electronics industries, where precision and repeatability are crucial. Conventional brazing systems rely on fixed parameter values and manual monitoring, leading to inconsistencies in joint quality, inefficient energy consumption, and higher defect rates. This study addresses these issues by developing an AI-driven smart control system that integrates machine learning and deep learning to optimize the brazing process, detect defects, and enable predictive maintenance. The system employs convolutional neural networks (CNNs) for computer vision-based defect classification and anomaly detection algorithms to identify deviations in real time. Experimental results demonstrate that the proposed system enhances defect detection accuracy to 97.2%, improves process efficiency by 23.5%, and reduces energy consumption by 18.7%. These findings highlight AI’s potential to improve brazing reliability and efficiency. However, computational overhead remains a limitation. future research will focus on enhancing scalability and real-time adaptability to facilitate broader industrial adoption and seamless integration into manufacturing environments.

关键词： Accuracy Computational modeling Process control control systems Brazing Real-time systems Manufacturing Optimization Predictive maintenance Defect detection

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future of AI-Driven Surgical Robotics

Future of AI-Driven Surgical Robotics

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Disruptive Technologies (ICDT), International conference on

作者： Shivanshi Nigam Madhuri Gupta Sonali Srivastava Mohammad Asad Khan Sameer Malik Richa Chaturvedi School of Computer Science Engineering and Technology Bennett University Greater Noida India Samsara San Francisco California USA University at buffalo New York USA

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Artificial Intelligence (AI)-driven surgical robotics is revolutionizing modern medicine by enhancing precision, reducing human error, and enabling minimally invasive procedures with improved patient outcomes. AI-powered robots leverage real-time decision-making, adaptive control, and autonomous task execution, surpassing traditional surgical methods. With applications across various specialties, AI-driven systems like the Da Vinci Surgical System and Smart Tissue Autonomous Robot (STAR) are already reshaping surgeries, offering better precision and reduced recovery times. This paper explores the motivations behind integrating AI into surgical robotics, its current applications, and the potential for fully autonomous surgeries. Despite challenges such as data quality, high costs, and regulatory barriers, AI-driven surgical robotics holds promise for significantly improving patient care, particularly in underserved regions. By surveying existing systems, AI techniques, and emerging trends, this research highlights the future of surgery, offering insights into the transformative role AI will play in healthcare's evolution.

关键词： Medical robotics Minimally invasive surgery Costs Stars Precision engineering Medical services Machine learning Market research Real-time systems Artificial intelligence

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