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A Multi-Agent Approach for Adaptive Finger Cooperation in Learning-based In-Hand Manipulation

A Multi-Agent Approach for Adaptive Finger Cooperation in Le...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lingfeng Tao Jiucai Zhang Michael Bowman Xiaoli Zhang Intelligent Robotics and Systems Lab Colorado School of Mines Golden CO GAC R&D Center Silicon Valley Sunnyvale CA USA

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning-based approaches mainly focus on training a single robot-structure-specific policy through the centralized learning mechanism, lacking adaptability to changes like robot malfunction. To solve this limitation, this work treats each finger as an individual agent and trains multiple agents to control their assigned fingers to complete the in-hand manipulation task cooperatively. We propose the Multi-Agent Global-Observation Critic and Local-Observation Actor (MAGCLA) method, where the critic can observe all agents' actions globally, and the actor only locally observes its neighbors' actions. Besides, conventional individual experience replay may cause unstable cooperation due to the asynchronous performance increment of each agent, which is critical for in-hand manipulation tasks. To solve this issue, we propose the Synchronized Hindsight Experience Replay (SHER) method to synchronize and efficiently reuse the replayed experience across all agents. The methods are evaluated in two in-hand manipulation tasks on the Shadow dexterous hand. The results show that SHER helps MAGCLA achieve comparable learning efficiency to a single policy, and the MAGCLA approach is more generalizable in different tasks. The trained policies have higher adaptability in the robot malfunction test compared to the baseline multi-agent and single-agent approaches.

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Multi-class Bearing Fault Classification using Deep Learning and Transfer Learning models

Multi-class Bearing Fault Classification using Deep Learning...

引用

Innovation in Technology (ASIANCON), Asian conference on

作者： Avanish Shimpi Gunjay Chitr Suhalka Sameer Sayyad Satish Kumar Arunkumar Bongale Dept.Robotics and Automation Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the modern age of industry 4.0 and state-of-the-art manufacturing, automated inspection plays an essential role. The bearing in the rotary machine is a vital and critical component as it provides support and stability to the rotary elements of machines. The defect in the bearing is responsible for the increase in vibrations, noise and reduction in reliability and safety of the rotating machines. It is very challenging to humanly detect different faults in these bearings. This work highlights the methods of multi-class bearing fault classification using Deep Learning (DL) and Transfer Learning (TL) models. The different DL models such as Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) are used along with the TL models such as VGG16 and MobileNet. The models perform with good classification accuracy ranging from 88% to 98%. The Transfer learning models perform better for both binary and multi-class fault classification of the bearings.

关键词： Deep learning Vibrations Technological innovation Accuracy Fault detection Transfer learning Stability analysis Safety Reliability Convolutional neural networks

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Modelling of Shear Walls with Opening Using Machine Learning Techniques

Modelling of Shear Walls with Opening Using Machine Learning...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Yogesh Aggarwal Department of Civil Engineering NIT Kurukshetra Kurukshetra Haryana India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

A Shear wall is the most appropriate structure shape in reinforced concrete buildings. Thus, examining the Shear Wall in structural systems is vital because it effectively resists lateral loads such as wind and seismic forces. The behaviour of such reinforced concrete building with Shear Wall differs from that of other reinforced concrete constructions. Thus, structural analysis of Shear Walls with openings for natural light, ventilation, entry, escape etc is required This factor has been analysed in terms of maximum storey displacement and base shear for model with Shear Walls and openings, using Equivalent static method to analyse the seismic performance of the structures. A comprehensive dataset is prepared using Staad Pro v8i software. The models are developed for the number of stories from 5 to 40, with different sizes of openings in the shear wall. A total of 240 models were analysed in III, IV and V zones of the earthquake, as defined by Indian code. The study involves the data set preparation and training, testing and validation using different machine learning techniques like deep neural networks, artificial neural networks and random forests. so that it will be helpful in predicting the seismic response of any similar multi-storey building in terms of Maximum storey displacement and Base shear.

关键词： Training Radio frequency Analytical models Buildings Artificial neural networks Predictive models Ventilation Concrete Random forests Testing

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A survey of state-of-the-art U-space research

A survey of state-of-the-art U-space research

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international conference on Autonomous Robots and Agents, ICARA

作者： Jes Hundevadt Jepsen Kristian Husum Laursen Kjeld Jensen SDU UAS Center University of Southern Denmark Odense Denmark

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

U-space, the EU implementation of air traffic management of drones, is one of the key enablers to realizing a safe integration of crewed and uncrewed aviation in shared airspace. The development and integration of U-space and its services will support a high level of digitalization and automation of drones and has been ongoing since 2017 when its blueprint was published by SESAR. Now with the Commission Implementing Regulation (EU) 2021/{ 664/665/666} in force, the establishment of U-space airspace in the EU is becoming a reality. This paper surveys available research and state-of-the-art on U-space. A total of 60 publications have been identified using the presented literature search method. Based on the insights gained from the literature search, eight subdomains have been defined. Each publication has been assigned to one primary subdomain and one or more secondary subdomain(s) within U-space. The subdomains are described and discussed and probable future research perspectives are extracted.

关键词： Surveys automation Search methods Force Regulation Air traffic control Robots

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Particle Swarm Optimization Based Control For a Controlled Floating Space Robot

Particle Swarm Optimization Based Control For a Controlled F...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： David Chaudhary Senthil Murugan Department of Aerospace Engineering Aero-electro(i) mechanics & systems lab Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

This paper explores Particle Swarm Optimization (PSO) based control of a morphing robot arm mounted on a Controlled Floating Space Robot (CFSR). CFSR is a new study spacecraft model allowing controlled adjustments of the satellite base's pose. The dynamic model of the chaser spacecraft is derived relative to the target in an inertial frame, using the Clohessy- Wiltshire model or Hills equations for defining their relative motion. It is assumed that both spacecraft are in an Inplane-elliptical formation. The manipulator dynamics are obtained through Lagrangian formulation. The PSO algorithm optimizes controller gain values while a quintic polynomial tra-jectory is provided as input to the arm joints. Results show that the PSO-based controller effectively reduces tracking error and power requirements, and it is also compared with a conventional proportional-derivative (PD) controller.

关键词： Space vehicles Satellites Trajectory tracking Arms Aerospace electronics Mathematical models Polynomials

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Design and Real-World Application of a Flexible Mobile Robot System for Urban Logistics

Design and Real-World Application of a Flexible Mobile Robot...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Lukas Müller Hao Pang Lars Ohnemus Maximilian Winter Kai Furmans Institute for Material Handling and Logistics Karlsruhe Institute for Technology Karlsruhe Germany SEW-EURODRIVE GmbH & Co KG Bruchsal Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

As the demand for online retail and food delivery services continues to grow, it becomes increasingly important to find efficient last-mile delivery solutions that can support the sustainability of urban areas. Autonomous Delivery Robots (ADRs) could potentially offer a solution to these challenges. However, long-term real-world studies are still required. This work presents a concept for a flexible ADR-based logistics system for urban areas, with a focus on the design of a robust ADR with detachable transport boxes and accompanying infrastructure. Here, we draw on well-established methods from autonomous mobile robotics in intralogistics. We present a two-mode delivery concept that removes the need for the recipient to be present at the time of delivery. This concept is currently being tested in the ongoing efeuCampus living lab in Bruchsal, Germany. We provide first findings and recommendations for further research in this area.

关键词： Urban areas Mobile robots Sustainable development Manufacturing automation Logistics

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Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning

Differentially Encoded Observation Spaces for Perceptive Rei...

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IEEE international conference on robotics and automation (ICRA)

作者： Lev Grossman Brian Plancher Berkshire Grey Bedford MA USA Barnard College Columbia University New York NY USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range from super-human level video game agents to dexterous, physically intelligent robots. However, training these perceptive DRL-enabled systems remains incredibly compute and memory intensive, often requiring huge training datasets and large experience replay buffers. This poses a challenge for the next generation of field robots that will need to be able to learn on the edge in order to adapt to their environments. In this paper, we begin to address this issue through differentially encoded observation spaces. By reinterpreting stored imagebased observations as a video, we leverage lossless differential video encoding schemes to compress the replay buffer without impacting training performance. We evaluate our approach with three state-of-the-art DRL algorithms and find that differential image encoding reduces the memory footprint by as much as 14.2× and 16.7× across tasks from the Atari 2600 benchmark and the DeepMind Control Suite (DMC) respectively. These savings also enable large-scale perceptive DRL that previously required paging between flash and RAM to be run entirely in RAM, improving the latency of DMC tasks by as much as 32%.

关键词： Training Video games Image coding Monte Carlo methods Memory management Random access memory Streaming media

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Comparison of Rating Scale and Pairwise Comparison methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Comparison of Rating Scale and Pairwise Comparison Methods f...

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IEEE international conference on robotics and automation (ICRA)

作者： Qiao Wang Ziqi Wang Marc G. Carmichael Dikai Liu Chin-Teng Lin Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity *** variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： Surveys Accuracy NASA Collaboration Reliability engineering Particle measurements Task analysis

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On Improving Boundary Quality of Instance Segmentation in Cluttered and Chaotic Scenarios

On Improving Boundary Quality of Instance Segmentation in Cl...

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IEEE international conference on robotics and automation (ICRA)

作者： Biqi Yang Xiaojie Gao Xianzhi Li Yun-Hui Liu Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology

Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and precise instance segmentation in these challenging scenarios. Technical-wise, we first formulate an IoU-based Boundary-aware Mask head (IBM head) for predicting the instance-level mask, boundary, and their corresponding IoU scores. With this core module, we then follow the coarse-to-fine strategy and design our pipeline with two stages: an 1IoUNet to learn localization-based objectness cue and a hierarchical mask refiner to produce sharper and cleaner boundaries. We deploy the IBM head throughout the framework. Extensive experimental results on three grasping benchmarks manifest that our method attains the best instance segmentation performance, compared with the state-of-the-art approaches. Practically, we conduct real-world picking tests to show that with the objectness and boundary IoU scores as guidance, we are able to filter invalid (occluded) instances and select high-fidelity (exposed) instances for grasping.

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Performance Analysis of ANFIS and DRL Based Speed Controllers for Sensorless Drives

Performance Analysis of ANFIS and DRL Based Speed Controller...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Kasim M. Al-Aubidy Abdullah F. Al-Saoudi Mechatronics Eng. Dept. Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Permanent magnet synchronous machines (PMSMs) are of great interest in automation and robotics applications due to their small size, high efficiency, and low maintenance requirements. However, these motors face challenges in controlling their speed and position, such as precise rotor detection. This situation is essential in many Industrial applications. Conventional controllers such as PID controllers are commonly used, but their performance is often affected by changes in system dynamics, necessitating updating of controller parameters based on the accurate mathematical model of the motor. This research aims to investigate several intelligent control strategies that do not rely on the mathematical model of the motor. The performance of the drive system will be evaluated using three intelligent controllers: a neural network controller, a neural fuzzy inference system (ANFIS) controller, and a deep reinforcement learning (DRL) controller. The performance of these smart controllers will be compared with that of the PID controller. The results presented in this paper demonstrate the ability of the proposed smart controllers to regulate the speed of PMSMs compared to a PID controller. Specifically, the ANFIS-based controller shows the best performance in terms of maximum overshoot, while the DRL controller demonstrates the best settling time.

关键词： automation Service robots System dynamics Velocity control Motors Robot sensing systems Control systems Mathematical models Maintenance Time factors

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