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Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning 8

Bridging the gap between Learning-to-plan, Motion Primitives...

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8th Conference on Robot Learning, CoRL 2024

作者： Kicki, Piotr Tateo, Davide Liu, Puze Guenster, Jonas Peters, Jan Walas, Krzysztof IDEAS NCBR Warsaw Poland Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Department of Computer Science Technische Universitat Darmstadt Germany

Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper functioning of robotic platforms and preventing unexpected behaviors. Recent advances in kinodynamic planning demonstrate that learning-to-plan techniques can generate complex and reactive motions under intricate constraints. However, these techniques necessitate the analytical modeling of both the robot and the entire task, a limiting assumption when systems are extremely complex or when constructing accurate task models is prohibitive. This paper addresses this limitation by combining learning-to-plan methods with reinforcement learning, resulting in a novel integration of black-box learning of motion primitives and optimization. We evaluate our approach against state-of-the-art safe reinforcement learning methods, showing that our technique, particularly when exploiting task structure, outperforms baseline methods in challenging scenarios such as planning to hit in robot air hockey. This work demonstrates the potential of our integrated approach to enhance the performance and safety of robots operating under complex kinodynamic constraints. © 2024 Proceedings of Machine Learning Research.

关键词： Reinforcement learning

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Mixed Reality Application in Robot Navigation and Control 16

Mixed Reality Application in Robot Navigation and Control

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Cismaru, Stefan-Irinel Petcu, Florina-Luminita Resceanu, Ionut-Cristian Roibu, Horatiu Trasculescu, Andrei-Costin Pana, Cristina-Floriana Bizdoaca, Nicu-George University of Craiova Faculty of Automation Computers and Electronics Mechatronics and Robotics Department Craiova Romania University of Craiova Faculty of Automation Computers and Electronics Computer Science Department Craiova Romania

ISBN: (纸本)9798350388107

Among the emerging technologies, Mixed Reality (MR) has provided the means to interact with holograms. A very distant future is now near and accessible, which allows for the replacement of classic controllers for robots and is driven by MR. Holograms involve dynamic 3D elements that are integrated into the environment, enhancing control means for the user. These elements may be visually attended to and interacted with via eye concentration or hand gestures, and they have the capability to execute a variety of activities. This emerging technology enables interactions that are otherwise unattainable in alternative settings or contexts, such as the presence of an avatar inside the program. Furthermore, it has the potential to be expanded to govern other mechatronic devices, including robots. The purpose of this work is to offer an improved and interactive method of controlling a mechatronic platform by using holograms, such that the adaptability of this technology may be shown in the context of operating real systems. Important for the authors of this paper is the broadening of the spectrum in which the application lies, thus contributing to the process followed for the development and applicability of the solution in key areas such as industry, training, and medicine. © 2024 IEEE.

关键词： Holograms

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Dynamic Subgoal Based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm robotics

Dynamic Subgoal Based Path Formation and Task Allocation: A ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Peter, Robinroy Ratnabala, Lavanya Andrew Charles, Eugene Yugarajah Tsetserukou, Dzmitry Ltd Jaffna Sri Lanka Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Moscow121205 Russia University of Jaffna Department of Computer Science Jaffna Sri Lanka

ISBN: (纸本)9781665410205

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A ∗ algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance. © 2024 IEEE.

关键词： Traffic congestion

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Comparative Analysis of Reinforcement Learning Techniques in Simulated robotics Environments 7

Comparative Analysis of Reinforcement Learning Techniques in...

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7th International Conference on Intelligent robotics and Control Engineering, IRCE 2024

作者： Yoseph, Abey Tanveer, M. Hassan Voicu, Razvan Cristian Shahid, M. Farrukh Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States National University of Computer and Emerging Sciences Department of Ai and Data Science Karachi Pakistan

ISBN: (纸本)9798350352399

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. The implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. The Python openai-ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. The agent's action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. The reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. The algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. The goal state at each episode was randomly generated and the robot's performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Neural Network in Dermatology: Pioneering Skin Lesion Classification 6

Deep Neural Network in Dermatology: Pioneering Skin Lesion C...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Fuad, Nasif Rakib, Abdur Al Noman, Abdullah Islam, Md Rafiqul Rahman, Mahbubur Refat, Md Abu Rumman Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331531973

Skin cancer is one of the most common cancer types in the world. Early detection of skin cancer is essential as it significantly improves the treatment outcomes. However, recognizing skin lesions is challenging due to the high degree of similarity in lesion's appearance, location, color, and size. In response to this challenge, we developed a deep learning based method for skin lesion classification by ensembling two pre-trained CNN models called ResNet50 and DenseNet201 using the HAM10000 dataset. With the imbalance in the dataset, we used the Synthetic Minority Over-sampling Technique(SMOTE) and class weighted approach along with the data Augmentation approach. As a result, our study demonstrates that the proposed approach attained a remarkable 96.0% accuracy on the HAM10000 dataset, surpassing other cutting-edge methods. © 2024 IEEE.

关键词： Deep neural networks

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

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6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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Deep Learning Based FOD Detection For Runway Health Monitoring 1

Deep Learning Based FOD Detection For Runway Health Monitori...

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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kumar, Amarjeet Mittal, Aanya Dixit, Abhishek Agrawal, Pooja School of Robotics Defence Institute of Advanced Technology Pune India Manipal University Jaipur Department of Computer Science Rajasthan India Defence Institute of Advanced Technology Department of Aerospace Pune India

ISBN: (纸本)9798350355611

This paper focuses on the safety of airport runways, which is crucial for aviation industries. Foreign Object Debris (FOD) i.e. objects like nails or bolts can cause significant harm to aircraft and potentially lead to catastrophic incidents and fatalities. This study delves into detecting objects on runways by using versions of the YOLO (You Only Look Once) model. Further, it also presents the differences in detecting objects and comparing the accuracy across a range of objects, paying attention to their ability to identify even the smallest things that have a potential danger. In order to identify the model variation and enable real-time runway surveillance, the proposed methodology considers the trade-off between detection speed and precision. The results illustrate the effectiveness of the latest YOLO model in object recognition over existing models, this can support the improvement of runway safety and the development of automated systems that monitor runway conditions. © 2025 IEEE.

关键词： Aircraft accidents

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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