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arXiv 2024年

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a 'scalable supervision' concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs - which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied - and (ii) aggregation across larger regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation

RFFCE: Residual Feature Fusion and Confidence Evaluation Net...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiwei Meng Shanshan Ji Shiqiang Zhu Tianlei Jin Te Li Jason Gu Wei Song Research Center for Intelligent Robotics Zhejiang Lab Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach consists of three primary innovations: residual feature fusion for representative RGBD feature extraction; confidence evaluation and confidence-based paired points offsets regression for self-evaluation and self-optimization respectively. Their effectiveness is verified through an ablation study, and our RFFCE achieves the SOTA performance on LineMOD, Occlusion-LineMOD and YCB-Video datasets. Additionally, we also conduct a real-world object grasping experiment for visualization and qualitative evaluation of the RFFCE.

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An Efficient and Secure Post-Quantum Multi-Authority Ciphertext-Policy Attribute-Based Encryption Method Using Lattice

An Efficient and Secure Post-Quantum Multi-Authority Ciphert...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bagchi, Prithwi Bera, Basudeb Maheshwari, Raj Das, Ashok Kumar Yau, David K. Y. Sikdar, Biplab International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Singapore University of Technology and Design Information Systems Technology and Design Singapore487372 Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9781665494274

This article deals with designing an efficient post-quantum lattice based encryption scheme that relies on the multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The security of the proposed scheme is based on the hardness of the ring learning with errors (RLWE) problem. The construction of the proposed scheme is done using the Shamir's threshold secret sharing along with the Lagrange interpolation during the key generation and decryption processes in order to achieve the segmentation and restoration of private keys. A comparative study with the existing state of art schemes has been performed to show the feasibility and efficiency of the proposed scheme. Furthermore, experiments on the proposed scheme have been conducted to illustrate the computational time required during the key generation, encryption and decryption processes. © 2023 IEEE.

关键词： Quantum cryptography

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OffRIPP: Offline RL-based Informative Path Planning

arXiv

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arXiv 2024年

作者： Gadipudi, Srikar Babu Deolasee, Srujan Kailas, Siva Luo, Wenhao Sycara, Katia Kim, Woojun The Department of Electrical Engineering Indian Institute of Technology Madras India The Robotics Institute Carnegie Mellon University United States The School of Interactive Computing Georgia Institute of Technology United States The Department of Computer Science University of North Carolina at Charlotte United States

Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution—key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms

arXiv

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arXiv 2025年

作者： Li, Hepeng Liu, Yuhong Yan, Jun Department of Electrical & Computer Engineering University of Maine Orono United States Santa Clara University Santa Clara United States Concordia Institute for Information Systems Engineering Concordia University Montreal Canada

Artificially intelligent (AI) agents that are capable of autonomous learning and independent decision-making hold great promise for addressing complex challenges across domains like transportation, energy systems, and manufacturing. However, the surge in AI systems’ design and deployment driven by various stakeholders with distinct and unaligned objectives introduces a crucial challenge: how can uncoordinated AI systems coexist and evolve harmoniously in shared environments without creating chaos? To address this, we advocate for a fundamental rethinking of existing multi-agent frameworks, such as multi-agent systems and game theory, which are largely limited to predefined rules and static objective structures. We posit that AI agents should be empowered to dynamically adjust their objectives, make compromises, form coalitions, and safely compete or cooperate through evolving relationships and social feedback. Through this paper, we call for a shift toward the emergent, self-organizing, and context-aware nature of these systems. © 2025, CC BY-NC-ND.

关键词： Decision making

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Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

arXiv

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arXiv 2023年

作者： Pandya, Ravi Wang, Zhuoyuan Nakahira, Yorie Liu, Changliu Robotics Institute Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot’s behavior, but such models do not account for the influence the robot has on the human’s actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot’s behavior based on a conditional model of the human that depends on the robot’s future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human’s intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users’ interactions with a robot collaborator quantitatively and qualitatively. © 2023, CC BY.

关键词： Microrobots

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Explainable Symbolic Regression Model for Tool Wear Diagnosis 9

Explainable Symbolic Regression Model for Tool Wear Diagnosi...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mishra, Debasish Han, Seulki Pattipati, Krishna R. Bollas, George M. University of Connecticut Department of Electrical & Computer Engineering 371 Fairfield Way StorrsCT06269 United States Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut 159 Discovery Dr. StorrsCT06269 United States University of Connecticut Department of Chemical & Biomolecular Engineering 159 Discovery Dr. StorrsCT06269 United States

ISBN: (纸本)9798350311402

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an R2 of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model for tool wear estimation. © 2023 IEEE.

关键词： Regression analysis

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Reliable Avionics Power System for Commercial Autonomous Science (CAS) microrovers 73

Reliable Avionics Power System for Commercial Autonomous Sci...

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73rd International Astronautical Congress, IAC 2022

作者： Khurana, Saksham Schweitzer, Lydia Newman, Kyle J. Guevara, Emilio Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States School of Computer Science Robotics Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States Department of Mechanical Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA United States

The realm of planetary exploration is gravitating towards a smaller class of rover. These microrovers are technically and scientifically ambitious and offer high return on investment. With mission resource constraints and limited mass, size, power, and communications, these rovers must leverage light-weight yet robust power systems which enable autonomous exploration initiatives, environmental survivability, fault detection, isolation, recovery, and ultimately mission success. This paper presents methodology, design, analysis and results pertaining to the power system of MoonRanger, the first lunar microrover for measuring ice at the Moon's south pole on a NASA Commercial Lunar Payload Services (CLPS) mission. Radioisotope Thermoelectric Generator (RTG) power that pertains to large rovers is not viable for microrovers due to mass, budget and availability. Instead, the rover is equipped with a custom designed solar panel with 30.7% cell efficiency able to sustain the rover's battery system for a full lunar day. Coupled with 200 Wh of lithium ion battery power, the rover's power system supports autonomous exploration into darkness for up to 4 hrs of dark survival. This custom power system is a fraction of the mass and cost of a traditional rover. Power generated by the solar panel is distributed to the rest of the rover system via compartmentalized power switching groups. This compartmentalization improves fault isolation for the rover's on-board fault detection and mitigation system as well as for operators on Earth. For missions with lower budget and time, like MoonRanger, rigorous testing and purchasing of space-rated parts is not as feasible. This power system organization can also be used to isolate the various power switching groups allowing for simpler testing, integration, and operational mode configuration. This paper describes how the rover's solar panel attributes, power generation, analysis, distribution, and storage systems facilitate MoonRanger's ambitio

关键词： NASA

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Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

arXiv

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arXiv 2023年

作者： Lekeufack, Jordan Angelopoulos, Anastasios N. Bajcsy, Andrea Jordan, Michael I. Malik, Jitendra Department of Statistics UC Berkeley United States Department of Electrical Engineering and Computer Science UC Berkeley United States Robotics Institute Carnegie Mellon University United States

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever;the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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