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The Journal of Machine Learning Research 2023年第1期24卷 5922-5982页

作者： Quentin Cappart Didier Chételat Elias B. Khalil Andrea Lodi Christopher Morris Petar Veličković Department of Computer Engineering and Software Engineering Polytechnique Montréal Montréal Canada CERC in Data Science for Real-Time Decision-Making Polytechnique Montréal Montréal Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT New York Department of Computer Science RWTH Aachen University Aachen Germany DeepMind London UK

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.

关键词： combinatorial optimization graph neural networks reasoning

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Improving safety in physical human-robot collaboration via deep metric learning

arXiv

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arXiv 2023年

作者： Rezayati, Maryam Zanni, Grammatiki Zaoshi, Ying Scaramuzza, Davide van de Venn, Hans Wernher Institute of Mechatronics Systems Zurich University of Applied Sciences Winterthur Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Robotics and Perception Group University of Zurich Zurich Switzerland

Direct physical interaction with robots is becoming increasingly important in flexible production scenarios, but robots without protective fences also pose a greater risk to the operator. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if there is physical contact or if a safety distance is violated. Although human injuries can be largely avoided in this way, all such solutions have in common that real cooperation between humans and robots is hardly possible and therefore the advantages of working with such systems cannot develop its full potential. In human-robot collaboration scenarios, more sophisticated solutions are required that make it possible to adapt the robot’s behavior to the operator and/or the current situation. Most importantly, during free robot movement, physical contact must be allowed for meaningful interaction and not recognized as a collision. However, here lies a key challenge for future systems: detecting human contact by using robot proprioception and machine learning algorithms. This work uses the Deep Metric Learning (DML) approach to distinguish between noncontact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations. The achieved results are promising and show show that DML achieves 98.6% accuracy, which is 4% higher than the existing standards (i.e. a deep learning network trained without DML). It also indicates a promising generalization capability for easy portability to other robots (target robots) by detecting contact (distinguishing between contactless and intentional or accidental contact) without having to retrain the model with target robot data. © 2023, CC BY-NC-SA.

关键词： Risk assessment

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vPlanSim: An Open Source Graphical Interface for the Visualisation and Simulation of AI systems 31

vPlanSim: An Open Source Graphical Interface for the Visuali...

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31st International Conference on Automated Planning and Scheduling, ICAPS 2021

作者： Roberts, Jamie O. Mastorakis, Georgios Lazaruk, Brad Franco, Santiago Stokes, Adam A. Bernardini, Sara School of Engineering Institute for Integrated Micro and Nano Systems University of Edinburgh United Kingdom EPSRC CDT in Robotics and Autonomous Systems Edinburgh Centre for Robotics United Kingdom Department of Computer Science Royal Holloway University of London United Kingdom

ISBN: (纸本)9781713832317

We introduce vPlanSim, an open source tool to aid in AI PDDL development. This tool is primarily aimed at researchers and developers who need a visual representation of their planning problem so that they can make useful insights into the performance of their system, and also to naturally convey their system to others. It is an open-source tool which allows a user to quickly and easily visualise a target environment to generate the problem files and also to visualise a plan. It is particularly well suited to spatial planning problems. This paper will demonstrate vPlanSim on 2D and 3D planning problems. vPlanSim is based on a small and carefully considered set of dependencies such as VTK and PyQt. It can be set up on different platforms and compiled from source with minimal effort. The code is and maintained via a clear code review mechanism. We welcome contributions from the open-source community. Copyright © 2021, Proceedings of the 31st International Conference on Automated Planning and Scheduling. All rights reserved.

关键词： Open systems

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Modelling and Control of a Trailer Sprayer for Precision Spraying

Modelling and Control of a Trailer Sprayer for Precision Spr...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： André Baltazar Filipe N. Santos Antόnio Paulo Moreira Salviano Pinto Soares M. J. C. S. Reis José Boaventura Cunha CRIIS-Centre for Robotics in Industry and Intelligent Systems INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal ECT—School of Science and Technology UTAD—University of Trás-os-Montes and Alto Douro Vila Real Portugal FEUP—Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Portugal Intelligent Systems Associate Laboratory (LASI) Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

关键词： Adaptation models Spraying Predictive models Valves real-time systems Agriculture Sensors

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Autonomous Building of Structures in Unstructured Environments via AI Planning 31

Autonomous Building of Structures in Unstructured Environmen...

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31st International Conference on Automated Planning and Scheduling, ICAPS 2021

作者： Roberts, Jamie O. Franco, Santiago Stokes, Adam A. Bernardini, Sara School of Engineering Institute for Integrated Micro and Nano Systems University of Edinburgh United Kingdom EPSRC CDT in Robotics and Autonomous Systems Edinburgh Centre for Robotics United Kingdom Department of Computer Science Royal Holloway University of London United Kingdom

ISBN: (纸本)9781713832317

In this paper, we offer a novel AI planning representation, based on a Cartesian coordinate system, for enabling the autonomous operations of Multi-Robot systems in 3D environments. Each robot in the system has to conform to unique actuation and connection constraints that create a complex set of valid configurations. Our approach allows Multi-Robot systems to self-assemble themselves into larger structures via AI planning, with the overarching goal of providing structural capabilities in harsh and uncertain environments. In comparing four different PDDL (Planning Domain Definition Language) domain representations, we show that our novel formulation satisfies the practical requirements emerging from robot deployment in the real world, resulting in an AI planning system that is accurate and efficient. We scale up performance by implementing direct FDR (Finite Domain Representation) generation based on the best performing PDDL model, bypassing the PDDL-to-FDR translation used by the majority of modern planners. The proposed approach is general and can be applied to a broad range of AI problems involving reasoning in 3D spaces. Copyright © 2021, Proceedings of the 31st International Conference on Automated Planning and Scheduling. All rights reserved.

关键词： Multipurpose robots

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Entropy Maximization in High Dimensional Multiagent State Spaces

Entropy Maximization in High Dimensional Multiagent State Sp...

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International Symposium on Multi-Robot and Multi-Agent systems (MRS)

作者： Ayhan Alp Aydeniz Enrico Marchesini Robert Loftin Kagan Tumer Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis Oregon USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Computer Science University of Sheffield Sheffield United Kingdom

Underwater or planetary exploration are prime examples of missions that can benefit from autonomous agents working together. However, discovering effective team-level behaviors (i.e., coordinated joint actions) is challenging in these domains as agents typically receive a sparse reward (zero-or constant-for the majority of the interactions). To address this issue, intrinsic rewards encourage agents to explore diverse policies to visit the state space more effectively. Unfortunately, as the agents’ state space grows, intrinsic reward-based (i.e., curiosity) approaches become less effective as they cannot effectively distinguish a diverse set of states. In this direction, we introduce state entropy maximization for multiagent learning where agents explore using local (dense) rewards and learn to solve the coordination task by leveraging global (sparse) rewards. Because of the intrinsic ability to balance local and global rewards, our approach enables the state entropy function to remain effective in high dimensional state spaces. Experiments in tightly coupled tasks requiring complex joint actions, show that local entropy-based rewards enable agents to discover successful team behaviors in high dimensional spaces where previous hand-tuned count-based rewards fail.

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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Select Informative Samples for Night-time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷 4310-4310页

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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Producing Lunar Steel and Oxygen using Molten Lunar Regolith Electrolysis

Producing Lunar Steel and Oxygen using Molten Lunar Regolith...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Patel, Palak B. Soto, Jose Adams, Zachary K. Koller, Scarlett Latyshev, Kir Boothe, Evan Cooper, Amber Fontanez, Jonatan Jalil, Laman Ma, Beverly Meza, Cesar Misquitta, Kristoff Na, Hyein Nguyen, Tom Pendley, Hezekiah Sangabattula, Lokesh Usey, Helena Zehner, Alice Allanore, Antoine Culpepper, Martin De Weck, Olivier Lordos, George Hoffman, Jeffrey Bergman, Dean Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology Sloan School of Management CambridgeMA02139 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Material Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Honeybee Robotics Exploration Systems AltadenaCA91001 United States

ISBN: (纸本)9781624107054

With the current Artemis missions planned, by 2028, NASA and its international and commercial partners may be establishing a permanent presence at the lunar South pole. The Artemis Base Camp will be the locus of international and public-private collaboration under the Artemis Accords, helping to bootstrap a lunar economy. As the lunar economy grows, traffic of people and goods will grow with it. Thus, we envision that NASA and its international and commercial partners will have increasing long-term needs for producing metal from in situ resources such as lunar regolith. These metals can be used to produce many end products on the moon including large pressure vessels, both for spacious habitats and industrial-scale storage and operations. In this paper, as a part of the NASA 2023 BIG Idea Challenge ‘Lunar Forge: Producing Metal Products on the Moon’, we design and manufacture a molten regolith electrolysis (MRE) reactor capable of producing various alloys of steel and other metal products from lunar regolith, with oxygen as a useful byproduct. We have built ARTEMIS Steelworks (Advancing Reactor Technologies for Electrolytic Manufacturing of In situ Steel), which consists of an electrolytic reactor, supporting the addition of alloying elements to produce molten steel from lunar regolith simulant. To prepare for a lunar technology demonstration, a preliminary test of the reaction on Earth was performed in a vacuum, a sonicator was tested to dislodge oxygen bubbles from the anode to increase electrode life, and an automated iron and slag collector was made to reduce the need for astronaut operation. The project’s technical objectives are to demonstrate steel-making capability while also quantifying energy efficiency, steel quality, and the expected useful life of the electrodes and vessel under different configurations relative to the state of the art. The comprehensive testing plan presented here includes functional subsystem and integrated testing in ambient conditio

关键词： Electrolysis

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