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Teaching Autonomous systems Hands-On: Leveraging Modular Small-Scale Hardware in the robotics Classroom

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arXiv 2022年

作者： Betz, Johannes Zheng, Hongrui Zang, Zirui Sauerbeck, Florian Zheng, Y. Rosa Biswas, Joydeep Walas, Krzysztof Dimitrov, Velin Behl, Madhur Krovi, Venkat Mangharam, Rahul The Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The Institute of Automotive Technology Technical University of Munich Germany The College of Engineering and Applied Science Lehigh University BethlehemPA18015 United States The Department of Computer Science University of Texas Austin AustinTX United States The Institute of Robotics and Machine Intelligence Poznan University of Technology Poland The Department of Computer Science University of Virginia CharlottesvilleVA United States College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29634 United States The Toyota Research Institute CambridgeMA02139 United States

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students’ computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course. © 2022, CC BY.

关键词： Students

METEOR: A Dense, Heterogeneous, and Unstructured Traffic Dataset with Rare Behaviors

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arXiv 2021年

作者： Chandra, Rohan Wang, Xijun Mahajan, Mridul Kala, Rahul Palugulla, Rishitha Naidu, Chandrababu Jain, Alok Manocha, Dinesh Department of Computer Science University of Maryland College Park United States Centre for Intelligent Robotics Indian Institute of Information Technology Allahabad India NavAjna Technologies Pvt. Ltd. India Department of Electrical and Computer Engineering University of Maryland College Park United States

We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios. METEOR consists of more than 1000 one-minute videos, over 2 million annotated frames with bounding boxes and GPS trajectories for 16 unique agent categories, and more than 13 million bounding boxes for traffic agents. METEOR is a dataset for rare and interesting, multiagent driving behaviors that are grouped into traffic violations, atypical interactions, and diverse scenarios. Every video in METEOR is tagged using a diverse range of factors corresponding to weather, time of the day, road conditions, and traffic density. We use METEOR to benchmark perception methods for object detection and multi-agent behavior prediction. Our key finding is that state-of-the-art models for object detection and behavior prediction, which otherwise succeed on existing datasets such as Waymo, fail on the METEOR dataset. METEOR marks the first step towards the development of more sophisticated perception models for dense, heterogeneous, and unstructured scenarios. © 2021, CC BY.

关键词： Multi agent systems

Multimodal reinforcement learning for partner specific adaptation in robot-multi-robot interaction

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Multimodal reinforcement learning for partner specific adapt...

IEEE-RAS International Conference on Humanoid Robots

作者： Murat Kirtay Verena V. Hafner Minoru Asada Anna K. Kuhlen Erhan Oztop Department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Department of Computer Science Adaptive Systems Group Humboldt-Universität zu Berlin Berlin Germany Science of Intelligence Research Cluster of Excellence Berlin Symbiotic Intelligent Systems Research Center Institute for Open and Transdisci-plinary Research Initiatives Osaka University Japan International Professional University of Technology in Osaka Osaka Japan Department of Psychol-ogy Humboldt-Universität zu Berlin Berlin Germany Ozyegin University Istanbul Turkey

ISBN: (纸本)9798350309805

Successful and efficient teamwork requires knowledge of the individual team members' expertise. Such knowledge is typically acquired in social interaction and forms the basis for socially intelligent, partner-adapted behavior. This study aims to implement this ability in teams of multiple humanoid robots. To this end, a humanoid robot, Nao, interacted with three Pepper robots to perform a sequential audio-visual pattern recall task that required integrating multimodal information. Nao outsourced its decisions (i.e., action selections) to its robot partners to perform the task efficiently in terms of neural computational cost by applying reinforcement learning. During the interaction, Nao learned its partners' specific expertise, which allowed Nao to turn for guidance to the partner who has the expertise corresponding to the current task state. The cognitive processing of Nao included a multimodal auto-associative memory that allowed the determination of the cost of perceptual processing (i.e., cognitive load) when processing audio-visual stimuli. In turn, the processing cost is converted into a reward signal by an internal reward generation module. In this setting, the learner robot Nao aims to minimize cognitive load by turning to the partner whose expertise corresponds to a given task state. Overall, the results indicate that the learner robot discovers the expertise of partners and exploits this information to execute its task with low neural computational cost or cognitive load.

关键词： Costs Humanoid robots Reinforcement learning Cognitive load Turning Biology Computational efficiency

The potential of thermal imaging to assist winemakers in the detection of downy mildew in Vitis vinifera cv Loureiro

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Procedia computer science 2025年 256卷 78-85页

作者： Fernando Portela Cláudio A. Paredes Joaquim J. Sousa Luís Pádua Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB) University of Trás-os-Montes e Alto Douro (UTAD) 5000-801 Vila Real Portugal PROMETHEUS Research Unit in Materials Energy and Environment for Sustainability Escola Superior Agrária Instituto Politécnico de Viana do Castelo (IPVC) 4900-347 Viana do Castelo Portugal Agronomy Department School of Agrarian and Veterinary Sciences UTAD 5000-801 Vila Real Portugal CISAS—Center for Research in Agrifood Systems and Sustainability IPVC 4900-347 Viana do Castelo Portugal Engineering Department School of Science and Technology UTAD 5000-801 Vila Real Portugal Centre for Robotics in Industry and Intelligent Systems (CRIIS) INESC Technology and Science (INESC-TEC) 4200-465 Porto Portugal Institute for Innovation Capacity Building and Sustainability of Agri-Food Production UTAD 5000-801 Vila Real Portugal

Vineyards represent one of the main crop systems of greatest importance in the world economy, both in terms of the value chain of wine production and in its current expression of generating tradable goods. One of the challenges of grape production is grapevine health, which annually forces the winegrowers to incur fixed costs associated with the prevention and/or treatment of pests and diseases, causing an impact on the environment due to the application of phytosanitary products. Among the various diseases affecting grapevines, downy mildew ( Plasmopara viticola ) stands out, which causes negative impacts throughout the phenological cycle, particularly on leaves by affecting the absorption of photosynthetic pigments and consequent chlorophyll production, ultimately influencing grape yield and quality. Traditional methods for detecting vineyard diseases are often insufficient in crop protection, favoring new forms of a more agile decision support with timely responses. Proximal sensing trough optical sensors can assist in early disease detection. In this context, thermal infrared sensors stand out for their ability to capture changes in thermal response of leaves due to changes in grapevine metabolism and photosynthetic production. This study aims to explore the use of thermal infrared imagery to assess the detection of downy mildew by evaluating temperature variations in the surface of grapevine leaves when compared with non-infected leaves in a vineyard plot of the cv. Loureiro. The effects of temporal and spatial variability on the temperature of mildew-infected vine leaves are reported. A temperature variation of 2.71 °C was found within the same leaf when comparing to infected and non-infect areas. As the infection progresses over time, thermal variation tends to increase.

关键词： proximity sensors thermography disease detection precision viticulture agricultural mechanization automation

Physics Informed Shallow Machine Learning for Wind Speed Prediction

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arXiv 2022年

作者： Lagomarsino-Oneto, Daniele Meanti, Giacomo Pagliana, Nicolò Verri, Alessandro Mazzino, Andrea Rosasco, Lorenzo Seminara, Agnese Department of Civil Chemical and Environmental Engineering University of Genoa Genoa Italy MaLGa Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy MaLGa Department of Mathematics University of Genoa Genoa Italy National Institute of Nuclear Physics Genoa Section Genoa Italy Center for Brains Minds and Machines Mit CambridgeMA United States Italian Institute of Technology Genoa Italy

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative datadriven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures. © 2022, CC BY-NC-SA.

关键词： Wind

Efficient State Abstraction using Object-centered Predicates for Manipulation Planning

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arXiv 2020年

作者： Agostini, Alejandro Lee, Dongheui Human-centered Assistive Robotics Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Intelligent and Interactive Systems Department of Computer Science University of Innsbruck Innsbruck6020 Austria Institute of Robotics and Mechatronics Wessling Germany

The definition of symbolic descriptions that consistently represent relevant geometrical aspects in manipulation tasks is a challenging problem that has received little attention in the robotic community. This definition is usually done from an observer perspective of a finite set of object relations and orientations that only satisfy geometrical constraints to execute experiments in laboratory conditions. This restricts the possible changes with manipulation actions in the object configuration space to those compatible with that particular external reference definitions, which greatly limits the spectrum of possible manipulations. To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency in manipulation tasks. This object-centered description is directly obtained from the poses and bounding boxes of objects using a novel learning mechanisms that permits generating signal-symbols relations without the need of handcrafting these relations for each particular scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Wave functions

Evacuation problem under the nuclear leakage accident

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arXiv 2021年

作者： Yao, Canqi Chen, Shibo Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node (bus pickup stop), 4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming (MINLP) problem. Then, to eliminate the difficulties of choosing a proper M value in Big-M method, a Big-M free method is employed to linearize the nonlinear terms of the MINLP problem. Finally, the resultant mixed integer linear program (MILP) problem is solvable with efficient commercial solvers such as CPLEX or Gurobi, which guarantees the optimal evacuation plan obtained. To evaluate the effectiveness of proposed evacuation model, we test our model on two different scenarios (a random one and a practical scenario). For both scenarios, our model attains executable evacuation plan within given 3600 seconds computation time. © 2021, CC BY-NC-SA.

关键词： Buses

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

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arXiv 2022年

作者： Gebhard, Timothy D. Bonse, Markus J. Quanz, Sascha P. Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany Max Planck ETH Center for Learning Systems Max-Planck-Ring 4 Tübingen72076 Germany ETH Zurich Institute for Particle Physics & Astrophysics Wolfgang-Pauli-Str. 27 Zurich8092 Switzerland Department of Computer Science ETH Zurich Zurich8092 Switzerland

Context. High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Aims. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Methods. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapt the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work;however, other (non-linear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors. Results. When we apply our method to four data sets from the VLT/NACO instrument, our algorithm provides a better false-positive fraction than PCA-based PSF subtraction, a popular baseline method in the field. Additionally, we find that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present first evidence that using the observing conditions as additional predictors can improve the results. Conclusions. Our HSR-based method provides an alternative, flexible and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data. Copyright © 2022, The Authors. All rig

关键词： Processing