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

作者： Zhaorun Chen Zhuokai Zhao Tairan He Binhao Chen Xuhao Zhao Liang Gong Chengliang Liu Department of Computer Science University of Chicago USA Robotics Institute Carnegie Mellon University USA Department of Mechanical Engineering Shanghai Jiao Tong University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Ensuring safety in Reinforcement Learning (RL), typically framed as a Constrained Markov Decision Process (CMDP), is crucial for real-world exploration applications. Current approaches in handling CMDP struggle to balance optimality and feasibility, as direct optimization methods can-not ensure state-wise in-training safety, and projection-based methods correct actions inefficiently through lengthy iterations. To address these challenges, we propose Adaptive Chance-constrained Safeguards (ACS), an adaptive, model-free safe RL algorithm using the safety recovery rate as a surrogate chance constraint to iteratively ensure safety during exploration and after achieving convergence. Theoretical analysis indicates that the relaxed probabilistic constraint sufficiently guarantees forward invariance to the safe set. And extensive experiments conducted on both simulated and real-world safety-critical tasks demonstrate its effectiveness in enforcing safety (nearly zero-violation) while preserving optimality (+23.8%), robustness, and fast response in stochastic real-world settings.

关键词： Adaptation models Markov decision processes Stochastic processes Optimization methods Reinforcement learning Probabilistic logic Robustness Safety Intelligent robots Convergence

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Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

Improving the Performance of Backward Chained Behavior Trees...

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

作者： Mart Kartasev Justin Salér Petter Ögren Robotics Perception and Learning Lab. School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Sweden

In this paper we show how to improve the performance of backward chained behavior trees (BTs) that include policies trained with reinforcement learning (RL). BTs represent a hierarchical and modular way of combining control policies into higher level control policies. Backward chaining is a design principle for the construction of BTs that combines reactivity with goal directed actions in a structured way. The backward chained structure has also enabled convergence proofs for BTs, identifying a set of local conditions to be satisfied for the convergence of all trajectories to a set of desired goal states. The key idea of this paper is to improve performance of backward chained BTs by using the conditions identified in a theoretical convergence proof to configure the RL problems for individual controllers. Specifically, previous analysis identified so-called active constraint conditions (ACCs), that should not be violated in order to avoid having to return to work on previously achieved subgoals. We propose a way to set up the RL problems, such that they do not only achieve each immediate subgoal, but also avoid violating the identified ACCs. The resulting performance improvement depends on how often ACC violations occurred before the change, and how much effort, in terms of execution time, was needed to re-achieve them. The proposed approach is illustrated in a dynamic simulation environment.

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Identification of Predictive Subnetwork for Brain Network-Based Psychiatric Diagnosis: An Information-Theoretic Perspective

Identification of Predictive Subnetwork for Brain Network-Ba...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kaizhong Zheng Shujian Yu Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an Department of Computer Science Vrije Universiteit Amsterdam Amsterdam

Graph neural networks (GNNs) have recently been applied to develop useful diagnostic tools for psychiatric disorders. However, due to the lack of interpretability, clinicians are hard to identify quantifiable and personalizable biomarkers which provide biologically and clinically relevance. We introduce three recently proposed GNN-based psychiatric disorders diagnostic models, namely BrainIB, Graph-PRI and CI-GNN, from an information-theoretic perspective. These models are able to discriminate psychiatric patients from healthy controls and identify predictive subgraph, a.k.a. biomarkers, solely from observations. We demonstrate their improved classification accuracy and interpretability on ABIDE database. We also put forward three proposals for future research.

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Reward Planning for Underactuated Robotic Systems: A Study on Pendubot with Parameters Uncertainty

Reward Planning for Underactuated Robotic Systems: A Study o...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Sinan Ibrahim Ramil Khusainov Ali Jnadi S. M. Ahsan Kazmi Institute of Robotics and Computer Vision Innopolis University Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia Computer Science and Creative Technologies University of the West of England (UWE) Bristol United Kingdom

There is a burgeoning interest in the field of Reinforcement Learning (RL) across various domains, including robotics. Traditional control approaches for robotic systems, such as semi-definite programming (SDP) or mixed integer programming (MIP), typically rely on the assumption of a known and deterministic model of the environment. In contrast, this study aims to introduce a novel RL method for planning the reward function specifically tailored for underactuated robotic systems with parameter uncertainty. We propose a method with a single RL agent to address the challenge of swing up and balance of a Pendubot system with uncertain parameters. The ultimate objective is to enhance the system’s ability to adapt and perform reliably in the face of varying uncertainties.

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Effects of Explanation Specificity on Passengers in Autonomous Driving

arXiv

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arXiv 2023年

作者： Omeiza, Daniel Bhattacharyya, Raunak Hawes, Nick Jirotka, Marina Kunze, Lars Oxford Robotics Institute University of Oxford United Kingdom Dept. of Computer Science University of Oxford United Kingdom

The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we investigate the effects of natural language explanations’ specificity on passengers in autonomous driving. We extended an existing data-driven tree-based explainer algorithm by adding a rule-based option for explanation generation. We generated auditory natural language explanations with different levels of specificity (abstract and specific) and tested these explanations in a within-subject user study (N=39) using an immersive physical driving simulation setup. Our results showed that both abstract and specific explanations had similar positive effects on passengers’ perceived safety and the feeling of anxiety. However, the specific explanations influenced the desire of passengers to takeover driving control from the autonomous vehicle (AV), while the abstract explanations did not. We conclude that natural language auditory explanations are useful for passengers in autonomous driving, and their specificity levels could influence how much in-vehicle participants would wish to be in control of the driving activity. © 2023, CC BY.

关键词： Autonomous vehicles

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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computer Modeling in Engineering & sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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Polarization-controlled dual resonant lattice Kerker effects

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Nano Research 2023年第2期16卷 3195-3200页

作者： Lei Xiong Xueqian Zhao Xiang Du Shuai Chen Yuanfu Lu Hongwei Ding Guangyuan Li School of Information Science and Engineering Yunnan UniversityKunming 650500China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Electronic and InformationGuangxi UniversityNanning 530004China College of Physics and Electronic Technology Anhui Normal UniversityWuhu 241000China Tianjin H-Chip Technology Group Corporation Tianjin 300467China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen 518055China

The generalized Kerker effects have attracted increasing interests in recent years due to their abilities to manipulate the far-field properties of ***,the dual-polarized generalized Kerker effect enabling different tailoring of orthogonally-polarized electromagnetic waves has not yet been ***,we demonstrate polarization-controlled dual resonant lattice Kerker effects in periodic silicon *** varying the incident angle,the electric dipole and magnetic dipole surface lattice resonances can spectrally overlap,causing zero reflectance and unitary transmittance,i.e.,the resonant lattice Kerker *** incident angle for achieving this effect can be tuned differently for s-and p-polarizations over large regions by varying the nanodisk size or the lattice *** proposed dual-polarized resonant lattice Kerker effects open up avenues for polarization-controlled manipulation of the phase and wavefront of light with metasurfaces.

关键词： Kerker effect surface lattice resonances dual-polarized silicon metasurface

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Trustworthy ROS Software Architecture for Autonomous Drones Missions: From RoboChart Modelling to ROS Implementation

Trustworthy ROS Software Architecture for Autonomous Drones ...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Nabil Shaukat Shival Dubey Bilal Kaddouh Andy Blight Lenka Mudrich Pedro Ribeiro Hugo Araujo Rob Richardson Louise Dennis Ana Cavalcanti Mohammad Mousavi Institute of Design Robotics and Manufacturing School of Mechanical Engineering University of Leeds UK Department of Computer Science University of York UK Department of Informatics Kings College London UK Department of Computer Science University of Manchester UK

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Drones are becoming essential tools in emergency situations such as search and rescue, surveillance, and firefighting. These applications make the development of trustworthy software a priority to increase efficiency, cut costs, and reduce risks, in future replacing human personnel in challenging areas. RoboChart, a platform-agnostic tool, brings numerous benefits to the robotics community by providing a language and high-level tool to describe a model via automatic verification and exhaustive testing of the model. However, when it comes to real robotics it is essential to use testing tools suitable for specific robotic software. In the context of the Robotic Operating System (ROS), it imposes its own design constraints, best practices, and communication requirements. This paper aims to apply the RoboChart modelling approach to autonomous firefighting drones, putting forward a low-level ROS software architecture that aligns with RoboChart models, ensuring the trustworthiness of the system during operation.

关键词： Mechatronics Software architecture Surveillance computer architecture Software Personnel Formal specifications Robots Testing Drones

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NUSense: Robust Soft Optical Tactile Sensor

arXiv

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arXiv 2024年

作者： Yergibay, Madina Mussin, Tleukhan Seitzhan, Saltanat Kenzhebek, Daryn Kappassov, Zhanat Soh, Harold Taunyazov, Tasbolat Robotics Department Institute of Smart Systems and Artificial Intelligence Nazarbayev University Astana Kazakhstan Dept. of Computer Science National University of Singapore Singapore

While most tactile sensors rely on measuring pressure, insights from continuum mechanics suggest that measuring shear strain provides critical information for tactile sensing. In this work, we introduce an optical tactile sensing principle based on shear strain detection. A silicone rubber layer, dyed with color inks, is used to quantify the shear magnitude of the sensing layer. This principle was validated using the NUSense camera-based tactile sensor. The wide-angle camera captures the elongation of the soft pad under mechanical load, a phenomenon attributed to the Poisson effect. The physical and optical properties of the inked pad are essential and should ideally remain stable over time. We tested the robustness of the sensor by subjecting the outermost layer to multiple load cycles using a robot arm. Additionally, we discussed potential applications of this sensor in force sensing and contact localization. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicones

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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