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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India

ISBN: (纸本)9781665462167

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. © 2023 IEEE.

关键词： Industry 4.0

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Social Robot Assistant for Group Interactions with Secondary School Students: A Participatory Design Study

Social Robot Assistant for Group Interactions with Secondary...

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Joint of the ACM Workshops at the International Conference on Intelligent User Interfaces 2025, IUI-WS 2025

作者： Adikari, Manith O'brien, Annie Hamilton, Suzanne Hennessey, Alexandra Vinanzi, Samuele Cangelosi, Angelo Department of Computer Science University of Manchester United Kingdom Manchester Centre for Robotics and AI United Kingdom Manchester Institute of Education University of Manchester United Kingdom School of Computing & Digital Technologies Sheffield Hallam University United Kingdom

The application of social robots in group settings is an emerging area of research with the potential to transform numerous fields, particularly education. This paper explores the potential of social robots as assistants in collaborative group interactions among secondary school students through a Participatory Design (PD) study. This was achieved by conducting a focus group (10 participants, ages 11 to 15 years) that included discussions, robot interactions, and co-design activities. The findings reveal the students' challenges in group interactions, and their perceptions of how robots could assist them. The first part of the focus group was a exploration and co-design stage to encourage participants to discuss about and interact with social robots for group collaboration. This stage highlighted some of the challenges students face during group work and how they believe a social robot could assist them. The second part of the focus group involved getting the participants to discuss and co-design robot behaviours for a specific group collaborative task. This revealed the participants emphasis on the robot behaviours being clear, specific and relevant for the task. These insights contribute to the design of effective social robots for group collaborative settings for this user group. © 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social robots

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Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control

arXiv

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arXiv 2025年

作者： Wang, Haochen Shi, Zhiwei Zhu, Chengxi Qiao, Yafei Zhang, Cheng Yang, Fan Ren, Pengjie Lu, Lan Xuan, Dong School of Computer Science and Technology Shandong University China Robotics Institute Carnegie Mellon University United States DeepCode Robotics China Department of Sports Shanghai Jiao Tong University China

Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed "IL+RL" training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and 90.7% success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Our project website: https://***/HAMLET/. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Distributed Monitoring and Control System of Gas Concentrations in Different Environment 24

Distributed Monitoring and Control System of Gas Concentrati...

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24th International Conference on Control Systems and computer science, CSCS 2023

作者： Furdu, Iulian Pruteanu, Eusebiu Nitu, Costica University Vasile Alecsandri of Bacau Department of Mathematics and Informatics Bacau Romania University Vasile Alecsandri of Bacau Department of Energy and Computer Science Bacau Romania Polytechnic University Department of Intelligent Robotics Bucharest Romania

ISBN: (纸本)9798350313390

Wireless Sensor Systems (WSN) is a broad, exciting area with new perspectives and growing growth over the past decades, where more research is being done. WSNs contain many (hundreds of thousands) of micro-sized, cheap chips, powered by low-cost wireless interconnected batteries. These chips are called nodes, which could be of several types, including acoustic, radar, low-frequency magnetic, thermal, and visual sampling frequencies. Many applications are based nowadays on WSN, such as environmental control, smart cities, wildlife monitoring, vehicles and infrastructure, natural disasters, home security, underwater investigations, military, airplane surveillance and drone body sensors. One of the aims of the paper is to develop a smart architecture based on intelligent microprocessor sensors, WSN802GCA/GPA and the software needed to operate with a TINI control unit. TINI allows this system to be connected to Ethernet via TCP/IP © 2023 IEEE.

关键词： Wireless sensor networks

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Discovering Multiple Algorithm Configurations

Discovering Multiple Algorithm Configurations

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IEEE International Conference on robotics and Automation (ICRA)

作者： Leonid Keselman Martial Hebert Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples which represent typical deployment conditions. Automatic tuning of these settings is traditionally known as algorithm configuration. In this work, we extend algorithm configuration to automatically discover multiple modes in the tuning dataset. Unlike prior work, these configuration modes represent multiple dataset instances and are detected automatically during the course of optimization. We propose three methods for mode discovery: a post hoc method, a multistage method, and an online algorithm using a multi-armed bandit. Our results characterize these methods on synthetic test functions and in multiple robotics application domains: stereoscopic depth estimation, differentiable rendering, motion planning, and visual odometry. We show the clear benefits of detecting multiple modes in algorithm configuration space.

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Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects

Grasp Anything: Combining Teacher-Augmented Policy Gradient ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Malte Mosbach Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive grasping from clutter, akin to human dexterity, is one of the longest-standing problems in robot learning. Challenges stem from the intricacies of visual perception, the demand for precise motor skills, and the complex interplay between the two. In this work, we present Teacher-Augmented Policy Gradient (TAPG), a novel two-stage learning framework that synergizes reinforcement learning and policy distillation. After training a teacher policy to master the motor control based on object pose information, TAPG facilitates guided, yet adaptive, learning of a sensorimotor policy, based on object segmentation. We zero-shot transfer from simulation to a real robot by using Segment Anything Model for promptable object segmentation. Our trained policies adeptly grasp a wide variety of objects from cluttered scenarios in simulation and the real world based on human-understandable prompts. Furthermore, we show robust zero-shot transfer to novel objects. Videos of our experiments are available at https://***/grasp_anything.

关键词： Training Instance segmentation Motor drives Object segmentation Reinforcement learning Robot sensing systems Motors

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Modeling Complex Object Changes in Satellite Image Time-Series: Approach based on CSP and Spatiotemporal Graphs 27

Modeling Complex Object Changes in Satellite Image Time-Seri...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Ayadi, Zouhayra Boulila, Wadii Farah, Imed Riadh RIADI Laboratory National School of Computer Science University of Manouba Manouba Tunisia ICube Laboratory University of Strasbourg Strasbourg France Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia

This paper proposes a method for automatically monitoring and analyzing the evolution of complex geographic objects. The objects are modeled as a spatiotemporal graph, which separates filiation relations, spatial relations, and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs using constraint satisfaction problems (CSP). The process is divided into four steps: first, the identification of complex objects in each satellite image;second, the construction of a spatiotemporal graph to model the spatiotemporal changes of the complex objects;third, the creation of sub-graphs to be detected in the base spatiotemporal graph;and fourth, the analysis of the spatiotemporal graph by detecting the sub-graphs and solving a constraint network to determine relevant sub-graphs. The final step is further broken down into two sub-steps: (i) the modeling of the constraint network with defined variables and constraints, and (ii) the solving of the constraint network to find relevant sub-graphs in the spatiotemporal graph. Experiments were conducted using real-world satellite images representing several cities in Saudi Arabia, and the results demonstrate the effectiveness of the proposed approach. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Complex geographic object Constraint Satisfaction Problem (CSP) Dynamic CSP Satellite Image Time-Series (SITS) Spatial graph (SG) Spatiotemporal graph (STG)

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“Thanks for the Practice!”: LLM-Powered Social Robot as Tandem Language Partner at University 25

“Thanks for the Practice!”: LLM-Powered Social Robot as Ta...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Ashita Ashok Barbara Bruno Tamara Helf Karsten Berns Department of Computer Science University of Kaiserslautern-Landau Kaiserslautern Germany Institute of Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Large language models (LLMs), when integrated into social robots, have the potential to transform robot-assisted language learning by offering personalized, interactive communication. However, there is limited research exploring their potential to simultaneously reduce anxiety and enhance language-speaking skills among international university students, who often feel anxious when speaking a foreign language. This study addresses this gap by evaluating the impact of a humanoid robot powered by the OpenChat-3.5 LLM as a tandem partner for German language learning. Using a between-subjects design with 22 multilingual participants, two interaction conditions were tested: immersive (German-only) and bilingual (German-English). Our findings indicate that participants in the immersive mode reported experiencing significantly reduced perceived judgment by the robot compared to the bilingual mode. Although female participants showed a trend of greater improvement in learning gain, no significant gender differences were found. Open-ended feedback highlighted the need for enhanced contextual responses, slower speech rate, faster response times, and error corrections to enhance language speaking support. This study aims to advance social robots for learning by demonstrating the usage of generative AI in creating non-judgmental language practice scenarios.

关键词： Generative AI Large language models Social robots Humanoid robots Transforms Speech enhancement Speech Market research Multilingual Time factors

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Deep Reinforcement Learning for Network Security Applications With A Safety Guide

Deep Reinforcement Learning for Network Security Application...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Zhibo Lu, Xiaozhen Chen, Yuhan Xiao, Yilin Xiao, Liang Bu, Yanling Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Xiamen University Department of Informatics and Communication Engineering Xiamen China

ISBN: (纸本)9798350345384

Most of the typical reinforcement learning algorithms help wireless devices choose the security policy such as the moving strategy and communication policy by exploring all the possible state-action pairs including the risky policies that cause a severe collision or network disaster. In this paper, we design a safe reinforcement learning algorithm for safety-critical applications (e.g., intelligent transportation systems) to guide the learning agent to avoid exploring risky policies. This algorithm uses Q-network (i.e., a convolutional neural network or a deep neural network) to choose the policy and designs a safety guide to modify the chosen policy that results in dangerous status. More specifically, the safety guide includes a risk alarm module that evaluates the immediate warning value corresponding to the risk of each state-action pair and a G-network that estimates the long-term risk value. By adding the long-term risk value and the long-term expected reward output by the Q-network, this algorithm uses a safety dock to modify the chosen policy. This algorithm uses the immediate warning value to formulate a safe buffer and a risky buffer for the G-network updating to ensure fully exploration in the initial learning process. As a case study, we apply the designed algorithm in a cargo transportation system, in which the experimental results verify the effectiveness of our algorithm compared with the benchmark safe deep Q-network. © 2023 IEEE.

关键词： Network security

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Fusion of Inertial Sensor Suit and Monocular Camera for 3D Human Pelvis Pose Estimation

Fusion of Inertial Sensor Suit and Monocular Camera for 3D H...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mihaela Popescu Kashmira Shinde Proneet Sharma Lisa Gutzeit Frank Kirchner Robotics Group Faculty of Mathematics and Computer Science University of Bremen Bremen Germany Robotics Innovation Center German Research Center for Artificial Intelligence (DFKI GmbH) Bremen Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

In real-world scenarios, robots come closer to humans in many applications, sharing the same workspace or even manipulating the same objects. To ensure safe and intuitive collaboration, it is crucial to have an accurate knowledge of the human’s 3D position in space, which should be estimated with high precision, high frequency and low latency. However, individual sensors such as inertial measurement units (IMUs) or cameras cannot meet all requirements for reliable human pose estimation under conditions such as long operating times, large distances and occlusions. In this study, we highlight the limitations of different visual pose methods and present a fused approach for real-time estimation of the 3D position of the human pelvis using machine learning-based visual pose from a monocular camera and an IMU sensor suit. The multimodal fusion is based on the Invariant Extended Kalman filter (InEKF) on Lie Groups, which fuses drift-free visual poses with high-frequency inertial measurements in a loosely-coupled manner. The evaluation is performed on a recorded dataset of multiple subjects performing various experimental scenarios. The results show that the fused approach can increase the accuracy and robustness of the estimates, taking a step closer towards smooth human-robot collaboration.

关键词： Visualization Three-dimensional displays Accuracy Pose estimation Robot vision systems Sensor fusion Cameras Real-time systems Pelvis Robots

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