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arXiv 2023年

作者： Soni, Raghav Harnack, Daniel Isermann, Hauke Fushimi, Sotaro Kumar, Shivesh Kirchner, Frank Department of Electronics Engineering Indian Institute of Technology Banaras Hindu University Varanasi India DFKI GmbH Robotics Innovation Center Bremen Germany Undergraduate Course Program of Mechanical and Systems Engineering Kyoto University Kyoto Japan

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Exploring Multimodal Gait Rehabilitation and Assistance through an Adaptable Robotic Platform

Exploring Multimodal Gait Rehabilitation and Assistance thro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sophia Otálora Sergio D. Sierra M. Felipe Ballén-Moreno Marcela Múnera Carlos A. Cifuentes Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Robotics & Multibody Mechanics (R&MM) Reasearch Group Vrije Universiteit Brussel Brussels Belgium Flanders Make Brussels Belgium Biomedical Engineering Program Colombian School of Engineering Julio Garavito Bogotá D.C. Colombia The School of Engineering Science and Technology Universidad del Rosario Bogotá D.C. Colombia

Lower-limb exoskeletons and smart walkers are robotic devices to assist patients in regaining their autonomy after a stroke. The integration of these devices enables gait rehabilitation and functional compensation, promoting natural over-ground walking. This article presents the Adaptable Robotic Platform for Gait Rehabilitation and Assistance (AGoRA V2 platform), which integrates the new AGoRA V2 Smart Walker and the AGoRA V2 unilateral lower-limb exoskeleton. It was evaluated with 14 healthy subjects using physiological and kinematic variables and a perception assessment. The study entailed four conditions: Without exoskeleton (WOE), With Exoskeleton (WE&T), With Walker (WW), and With Platform (WP). Results indicate a reduction in the muscle activity of the Rectus Femoris (18%) and Vastus Lateralis (15%), comparing WE&T and WP, as well as walking without any device (WOE) and using any robotic device (WE&T, WW, WP). Results suggest the importance of combining the exoskeleton with the robotic walker and the assistance of each device independently. Moreover, using the complete platform induces slower gait patterns than the walker, as the mean impulse force and linear velocity decrease by 42% and 44%, respectively. These results demonstrate that the platform contributes to safety, and improvements in gait parameters and muscular activity, indicating the system's potential to act as a modular device according to users' conditions and therapeutic goals.

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Fiber-Optic and IMU Sensors for Muscle Fatigue Detection in Work Settings

Fiber-Optic and IMU Sensors for Muscle Fatigue Detection in ...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Sophia Otálora Marcelo Eduardo Vieira Segatto Maxwell E. Monteiro Carlos A. Cifuentes Camilo A.R. Diaz Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Campus Serra Instituto Federal do Espirito Santo (IFES) Serra Brazil Bristol Robotics Laboratory University of the West of England Bristol UK

Detection of muscle fatigue is essential in industrial environments to prevent work-related disorders and improve worker safety. Also, measuring elbow position is essential in human motion analysis. This paper proposes a novel approach for detecting muscle fatigue using an Optical Fiber (OF) sensor and Inertial Measurement Unit sensors (IMU). Ten healthy subjects performed a repetitive lifting activity using the IMU and OF sensor until reaching muscle fatigue. The study evaluates four machine learning algorithms, including Ensemble Classifiers (EC), Support Vector Machine (SVM), k Nearest Neighbour (kNN), and Neural Networks (NN). Results of detecting muscle fatigue using the OF and IMU sensors obtained an accuracy of 98.4%. This setup can be easily used by workers and be relevant to identify workers' fatigue during long working shifts.

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ERASOR: Egocentric ratio of pseudo occupancy-based dynamic object removal for static 3D point cloud map building

arXiv

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arXiv 2021年

作者： Lim, Hyungtae Hwang, Sungwon Myung, Hyun Daejeon34141 Korea Republic of Robotics Program at KAIST Daejeon34141 Korea Republic of

Scan data of urban environments often include representations of dynamic objects, such as vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud map with sequential accumulations of the scan data, the dynamic objects often leave unwanted traces in the map. These traces of dynamic objects act as obstacles and thus impede mobile vehicles from achieving good localization and navigation performances. To tackle the problem, this paper presents a novel static map building method called ERASOR, Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal, which is fast and robust to motion ambiguity. Our approach directs its attention to the nature of most dynamic objects in urban environments being inevitably in contact with the ground. Accordingly, we propose the novel concept called pseudo occupancy to express the occupancy of unit space and then discriminate spaces of varying occupancy. Finally, Region-wise Ground Plane Fitting (R-GPF) is adopted to distinguish static points from dynamic points within the candidate bins that potentially contain dynamic points. As experimentally verified on SemanticKITTI, our proposed method yields promising performance against state-of-the-art methods overcoming the limitations of existing ray tracing-based and visibility-based methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Urban planning

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Optimal plant growth through thermo mechatronic analysis 26

Optimal plant growth through thermo mechatronic analysis

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26th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2022

作者： Calderón, J. Alan Tafur, Julio Barriga, Benjamín Alencastre, Jorge Solano, Gonzalo Urbizagástegui, Rodrigo Lozano, John Chancán, Marvin Applied Nanophysics Institute for Physics Technical University of Ilmenau Ilmenau98693 Germany Engineering Department Mechatronic Master Program Pontificia Universidad Católica del Peru Lima15088 Peru Federal University Arkhangelsk163002 Russia Electrical Engineering and Robotics School Queensland University of Technology St LuciaQLD4072 Australia

ISBN: (纸本)9781950492640

This work is described as a proposal to apply modern control techniques and automation tools for optimal plant growth, also it was based on key agricultural strategies that were developed by ancient civilizations such as the Inca Empire. Many of them ancient techniques including the Inca engineering of andenes were forgotten or set aside through time. In this research, however, some of these key techniques are revisited to analyze and evaluate optimal plant growth using sensors and actuators that were not available in ancient civilizations. In addition, predictive and adaptive mathematical models are used for plant growth analysis of thermodynamic parameters such as temperature, humidity and potential of Hydrogen (pH). Furthermore, there were compared performances of sensors (electromechanical sensors) with designed sensors that were based in nanostructures, because of better study of the plant growth techniques. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Nanostructures

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Goal-Conditioned Reinforcement Learning Approach for Autonomous Parking in Complex Environments

Goal-Conditioned Reinforcement Learning Approach for Autonom...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Taeyoung Kim Taemin Kang Seungah Son Kuk Won Ko Dongsoo Har CCS Graduate School of Mobility Korea Advanced Institute of Science and Technology(KAIST) Daejeon Rep. of Korea The Robotics Program Korea Advanced Institute of Science and Technology(KAIST) Daejeon Rep. of Korea Department of Future Mobility Engineering Halla University Wonju Rep. of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In complex urban environments, autonomous vehicle parking is challenged by irregularly parked cars, static obstacles, and narrow spaces. Traditional parking methods are often limited to well-structured parking lots and fail to adapt to unstructured scenarios like alleyways with obstacles and randomly positioned vehicles. This paper addresses this limitation by applying goal-conditioned reinforcement learning to enable autonomous vehicles to park in congested environments based on specified target positions and orientations. A custom simulation environment is developed using Pygame to simulate parking scenarios across four progressive levels of difficulty, with obstacles to test adaptability. Three RL algorithms, SAC, HER, and an improved HER variant, are implemented to compare the performance in the simulation environment. Experimental results demonstrate that the proposed approach significantly improves parking success rates and trajectory efficiency in complex scenarios, contributing to robust, adaptable parking solutions for autonomous vehicles in real-world applications.

关键词： Training Space vehicles Three-dimensional displays Urban areas Reinforcement learning Trajectory Reliability Vehicle dynamics Autonomous vehicles Testing

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Real-Time Wheelchair Controller Based on POF-Based Pressure Sensors

Real-Time Wheelchair Controller Based on POF-Based Pressure ...

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Latin American Electron Devices Conference (LAEDC)

作者： A.X. González-Cely C.F. Blanco-Díaz Díaz Camilo A.R. T Bastos-Filho Telecommunications Laboratory Graduate Program in Electrical Engineering Federal University of Espírito Santo Av. Fernando Ferrari 514-Goiabeiras Vitoria Brazil Robotics and Assistive Technology Laboratory Graduate Program in Electrical Engineering Federal University of Espírito Santo Av. Fernando Ferrari 514-Goiabeiras Vitoria Brazil

The design of electrical wheelchair controllers remains a challenge for the scientific community. In recent years, Polymer Optical Fiber (POF)-based sensors have been used for biomedical applications, however, the application of these types of sensors for the control of assistive devices has been little explored. For this reason, this work proposes the design and validation of a fuzzy logic controller that uses information from an array of POF-based sensors located in a pillow, which is used for wheelchair control in four directions (right, left, forward and stop) by using neck movements. The results allow concluding that the system is fast, accurate, and reliable. This work presents a contribution related to the design of assistive devices based on optical fiber sensors and focuses on people with upper- and lower-limb mobility restrictions. Future studies will address the evaluation of the system for people with disabilities.

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Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning

arXiv

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arXiv 2025年

作者： Lee, Younghwan Luu, Tung M. Lee, Donghoon Yoo, Chang D. Electrical Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. To address this, we propose Reward Generation via Large Vision-Language Models (RG-VLM), which leverages the reasoning capabilities of LVLMs to generate rewards from offline data without human involvement. RG-VLM improves generalization in long-horizon tasks and can be seamlessly integrated with the sparse reward signals to enhance task performance, demonstrating its potential as an auxiliary reward signal. © 2025, CC BY.

关键词： Reinforcement learning

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Human Following Robot Based on Object Detection for Aerospace Application

Human Following Robot Based on Object Detection for Aerospac...

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Aerospace Electrical Engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Arwin Datumava Wahvudi Sumari Ahnaf Bagus Aryandika Ndaru Atmi Purnami Denny Dermawan Ika Noer Syamsiana Department of Electrical Engineering Cognitive Artificial Intelligence Research Group State Polytechnic of Malang Malang Indonesia Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Digital System Microprocessor Microcontroller and Programmable Logic Devices Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications.

关键词： YOLO Tensors Accuracy Prototypes Wheels Cameras Hardware Maintenance Aircraft Aerospace industry

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Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM

arXiv

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arXiv 2023年

作者： Lim, Hyungtae Kim, Beomsoo Kim, Daebeom Lee, Eungchang Mason Myung, Hyun School of Electrical Engineering KAIST [Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Artificial Intelligence Hanyang University Seoul04763 Korea Republic of Robotics Program at KAIST Daejeon34141 Korea Republic of

Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called Quatro++. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher success rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation significantly helps to increase the success rate for the ground vehicles. Finally, we apply our proposed method to the loop closing module in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitability of our method as an initial alignment in the loop closing. Our code is available at https://***. © 2023, CC BY-NC-ND.

关键词： Ground vehicles

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