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

作者： Junjia Liu Zhihao Li Wanyu Lin Sylvain Calinon Kay Chen Tan Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Idiap Research Institute Martigny Switzerland

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pretrained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot's mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations.

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A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

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IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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A Novel IoT-Enabled Oxygen Delivery System with Neural Network-Based Predictive Control

A Novel IoT-Enabled Oxygen Delivery System with Neural Netwo...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Vijai Sivalingam Jayakumar Jayaraj Subha Hency Jose Paul Geetika Yadav K. Rajasekaran P. Anantha Christu Raj Division of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India Indian Council for Medical Research New Delhi India Department of Biomedical Engineering KPR Institute of Engineering and Technology Coimbatore India Division of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In this research study, a hardware is designed to monitor and control oxygen delivery based on physiological parameters, blood Oxygen saturation, and heart rate for real time applications. The development and evaluation of an intelligent portable Oxygen Concentrator system that integrates a Sensor, Motor driver Controller, and ESP8266 microcontroller. The controller measures SPO 2 and heart rate. Data from these sensors are processed by neural network model implemented on the ESP8266 which predicts the optimal Oxygen flow rate constrained internet of things (IoT) applications such as wireless sensor networks. Therefore, in this paper, the design steps to build a model in NodeMCU ESP8266 using an artificial neural network (ANN) are presented. Controller is used in our study and NodeMCU ESP8266 is chosen because it is suitable to be used as a sensor node or sink in WSN applications. ANN with a Levenberg Marquardt algorithm is used as it can model the non - linearity with high accuracy. This intelligent system effectively manages Oxygen delivery, optimizing patient care, and has demonstrated high accuracy MSE of 0.0588 in predicting Oxygen flow requirements using a dataset of 1178 data points. For physiological metrics like SPO 2 , heart rate, respiration rate, and temperature, combining neural networks with data sets can help lower mistakes and increase the precision of predictions generated using these methods.

关键词： Heart rate Temperature sensors Wireless sensor networks Oxygen Accuracy Artificial neural networks Predictive models Data models Internet of Things Biomedical monitoring

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A Transferable Multi-Agent Reinforcement Learning Method for Distribution Service Restoration

A Transferable Multi-Agent Reinforcement Learning Method for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruiqi Si Ji Qiao Xiaohui Wang Kaixuan Ji Zibo Wang Zhang Jun Xuanying Pan Zhengyan Zhang School of Electrical Engineering and Automation Wuhan University Wuhan Hubei China Department of Artificial Intelligence Application China Electric Power Research Institute Beijing China

The occurrence of extreme events, which has increased the risk of major outages in the grid, makes the quick and efficient recovery of load in the distribution network become a key issue. The data-driven deep reinforcement learning method has great potential in providing fast decision-making. However, a large number of agents lead to the curse of dimensionality, making it inefficient to obtain effective control strategies. When tackling similar tasks in different power gird, the retraining of multiple agents will bring us great cost. To solve this problem, we propose a transferable multi-agent reinforcement learning framework that employs model reload and buffer reuse methods to transfer control strategies from small-scale simple scenes to large-scale complex scenes. It also utilizes attention mechanisms to aggregate observation features and handles the problem of variable observation dimensions. Finally, the distribution service restoration problem is modeled as a Markov decision process and solved using the QMIX algorithm. The performance of the proposed method has been verified in IEEE 34-node and IEEE 123-node distribution systems.

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Kinova Gemini: Interactive Robot Grasping with Visual Reasoning and Conversational AI

Kinova Gemini: Interactive Robot Grasping with Visual Reason...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Chen, Hanxiao Wang, Jiankun Meng, Max Q.-H. Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong in Shenzhen Shenzhen518057 China

ISBN: (纸本)9781665481090

To facilitate recent advances in robotics and AI for delicate collaboration between humans and machines, we propose the Kinova Gemini, an original robotic system that integrates conversational AI dialogue and visual reasoning to make the Kinova Gen3 lite robot help people retrieve objects or complete perception-based pick-and-place tasks. When a person walks up to Kinova Gen3 lite, our Kinova Gemini is able to fulfill the user's requests in three different applications: (1) It can start a natural dialogue with people to interact and assist humans to retrieve objects and hand them to the user one by one. (2) It detects diverse objects with YOLO v3 and recognize color attributes of the item to ask people if they want to grasp it via the dialogue or enable the user to choose which specific one is required. (3) It applies YOLO v3 to recognize multiple objects and let you choose two items for perception-based pick-and-place tasks such as 'Put the banana into the bowl' with visual reasoning and conversational interaction. © 2022 IEEE.

关键词： robotics

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An Attention-LSTM-based Fault Diagnosis Method for Satellite Attitude Control System

An Attention-LSTM-based Fault Diagnosis Method for Satellite...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Siyuan Ma Sheng Gao Xiaozhong Zuo Zhengyang Lv Chenyang Zou State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai China College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This study proposes a fault diagnosis method based on attention-LSTM for the satellite attitude control system. First, the hidden correlation information between high-dimensional variables is exploited through the variable attention layer. Then, deep temporal features are extracted with using the LSTM network layer. Finally, the time attention layer is introduced to increase the weight of important temporal information, effectively avoiding the problem of being forgotten due to too long input sequence and too many deep temporal features. The identification accuracy of slow fault in satellite attitude control system can be improved. The method is validated using data from the satellite semi-physical simulation platform, which demonstrates the effectiveness of this method.

关键词： Fault diagnosis Analytical models Visualization Satellites Recurrent neural networks Correlation Attitude control

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Quadrotor Autonomous Landing on Moving Platform

Quadrotor Autonomous Landing on Moving Platform

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Pengyu Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Control Science and Engineering Shandong University Jinan China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong

This paper introduces a quadrotor's autonomous take-off and landing system on a moving platform. The designed system addresses three challenging problems: fast pose estimation, restricted external localization, and effective obstacle avoidance. Specifically, first, we design a landing recognition and positioning system based on the AruCo marker to help the quadrotor quickly calculate the relative pose;second, we leverage a gradient-based local motion planner to generate collision-free reference trajectories rapidly for the quadrotor;third, we build an autonomous state machine that enables the quadrotor to complete its take-off, tracking and landing tasks in full autonomy;finally, we conduct experiments in simulated, real-world indoor and outdoor environments to verify the system's effectiveness and demonstrate its potential. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Motion planning

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MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

arXiv

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arXiv 2023年

作者： Shao, Shuwei Pei, Zhongcai Chen, Weihai Sun, Dingchi Chen, Peter C.Y. Li, Zhengguo The School of Automation Science and Electrical Engineering Beihang University Beijing China The Department of Mechanical Engineering National University of Singapore Singapore The SRO department Institute for Infocomm Research 1 Fusionopolis Way Singapore

Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, e.g., occlusion and dynamic objects. In this work, we introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process. Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually adds noise to the depth map generated by a pre-trained teacher model. Moreover, the teacher model allows applying a distillation loss to guide the denoised depth. Further, we develop a masked visual condition mechanism to enhance the denoising ability of model. Extensive experiments are conducted on the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior state-of-the-art competitors. The source code will be available at https:// ***/ShuweiShao/MonoDiffusion. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Neural Network based Realtime Walking Speed Estimation and Gait Phase Detection using Smart Insoles

Neural Network based Realtime Walking Speed Estimation and G...

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IEEE India Conference (INDICON)

作者： Debadrata Sarkar Abhijit Singh Sagnik Chakraborty Shibendu Shekhar Roy Aman Arora Robotics and Automation Group CSIR- Central Mechanical Engineering Research Institute (CMERI) Durgapur India Department of Mechanical Engineering National Institute of Technology Durgapur Durgapur India

ISBN: (纸本)9781665452724

Three machine learning based gait analysis models have been developed for real-time walking speed estimation, offline gait phase detection and real-time prediction of incoming gait cycle profile. All these have been developed by performing rigorous trials on multiple subjects to make these models deployable for any new subject with features within the extremum considered in model development. These utilize a single sensor based lucid and economical hardware for effective gait analysis in both clinical and outdoor environment. While the offline gait analysis provide insight into characteristics gait patterns of individuals, the real-time estimations and predictions are effective in designing control schemes for wearable assistive devices.

关键词： Legged locomotion Analytical models Biological system modeling Computational modeling Neural networks Estimation Machine learning

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research on improving requirement of renewable energy forecasting for system anti-disturbance

Research on improving requirement of renewable energy foreca...

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2022 Chinese automation Congress, CAC 2022

作者： Xiao, Qingming Yu, Dahai Li, Yan Li, Xutao Wang, Chao Ai, Dongping Ding, Zhenyu Nian, Ming The Technological Innovation Office of State Grid Ningxia Electric Power Co. Ltd. Yinchuan China Electric Power Research Institute Power System Department of China Beijing China Electric Power Research Institute Artificial Intelligence Department of China Beijing China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Yinchuan China Electrical & Automation Engineering Technical Institute of Capital Engineering Research Incorporation Ltd. Beijing China

ISBN: (纸本)9781665465335

Due to the integration of renewable energy, the maximum output of conventional power plants is reduced, and the change of peak valley difference is no longer periodic. In order to cope with the fluctuation of renewable energy, the system needs to increase the rotating reserve capacity. At present, power supply is mainly thermal power in China. The inflexibility of thermal power switch and the existence of minimum technical output increase the volatility of start-up response to renewable energy, and limit the output of renewable energy in the period of low load and renewable energy. Predicting the output power of renewable energy and reducing the uncertainty of renewable energy fluctuation is one of the effective means to reduce the redundant standby capacity of the system. The increase of reserve capacity is related to the prediction accuracy of output power of renewable energy stations. Therefore, renewable energy power prediction is of great significance to the safe and economic operation of power system. According to the requirements of relevant documents and regulations of the national energy administration, all grid connected renewable energy stations need to establish a renewable energy power prediction system. In this paper, hundreds of renewable energy power forecasting service providers has emerged. However, the performance and prediction accuracy of renewable energy power prediction results are uneven, there is a lack of unified test standards and test platform, and an effective integration mechanism and identification method have not been established. Therefore, it is necessary to establish relevant evaluation mechanisms and provide third-party evaluation services, so as to provide a fair reference for selecting strong renewable energy production scheduling support services. However, different from conventional power supply, renewable energy has random volatility, and large-scale renewable energy grid connection brings challenges to the security, stabilit

关键词： Renewable energy resources

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