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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 2981 条记录，以下是371-380 订阅

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Reinforced iLQR: A Sample-Efficient Robot Locomotion Learning

Reinforced iLQR: A Sample-Efficient Robot Locomotion Learnin...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Zong, Tongyu Sun, Liyang Liu, Yong NYU Elect & Comp Engn Dept Tandon Sch Engn Brooklyn NY 11201 USA

ISBN: (纸本)9781728190778

Robot locomotion is a major challenge in robotics. Model-based approaches are vulnerable to model errors, and incur high computation overhead resulted from long control horizon. Model-free approaches are trained with a large number of training samples, which are expensive to obtain. In this paper, we develop a hybrid control and learning framework, called Reinforced iLQR (RiLQR), which combines the advantages of model-based iLQR control with model-free RL policy learning to simultaneously achieve high sample efficiency, low computation overhead, and high robustness against model errors in robot locomotion. Through extensive evaluation on the Mujoco platform, we demonstrate that RiLQR outperforms the state-of-the-art model-based and model-free baselines by big margins in a set of tasks with different complexities.

关键词： Training Robust control computational modeling Conferences Cost function Robustness Complexity theory

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Learning Reachable Manifold and Inverse Mapping for a Redundant Robot manipulator

Learning Reachable Manifold and Inverse Mapping for a Redund...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Kim, Seungsu Perez, Julien NAVER Labs Europe 6 Chemin Maupertuis F-38240 Meylan France

ISBN: (纸本)9781728190778

Validating the kinematic feasibility of a planned robot motion and finding corresponding inverse solutions are time-consuming processes, especially for long-horizon manipulation tasks. Most existing approaches are based on solving iterative gradient-based optimization, so the processes are time-consuming and have a high risk of falling in local minima In this work, we propose a unified framework to learn a kinematic feasibility model and a one-shot inverse mapping model for a redundant robot manipulator. Once they are trained, the models can compute the kinematic reachability of a target pose and its inverse solutions without iterative process. We validate our approach using a 7-DOF robot arm with an object grasping application.

关键词： Robot motion Manifolds computational modeling Conferences Kinematics Grasping Manipulators

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A Novel Short-term Irradiance Forecasting Method based on Transformer Networks

A Novel Short-term Irradiance Forecasting Method based on Tr...

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international symposium on computational intelligence and Design, ISCID

作者： Aihua Yu Zhiwen Cao Qinlong Zhang Minyu Huang Beiping Hou Gang Li School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou P.R. China

Short-term solar irradiance forecasting plays a critical role in the effecitive integration of intermittent photovoltaic (PV) solar power into power grids. Existing research on short-term solar irradiance forecasting faces challenges such as instability and low computational efficiency. This study addressed this issue by proposing an improved compensated referenced transformer model. It focused on the region of interest according to the solar position and cloud velocity estimated using the optical flow technique. A sequence of VGG16 cloud features was used to train the transformer network with clear sky reference model. The experimental results show that our proposed method can reduce the normalised mean absolute percentage error of global horizontal irradiance prediction by 6.83%.

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Optimization of Multi-Resource Scheduling Strategies in Distributed Training Environments

Optimization of Multi-Resource Scheduling Strategies in Dist...

引用

Artificial intelligence and Intelligent Manufacturing (AIIM), international symposium on

作者： Dan Zhang Yu Wang Yong Cui Yan Jiang School of Software Shenyang University of Technology Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang Intelligent Robot Innovation Center Co. LTD Shenyang China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In large-scale model training, distributed training frameworks offer an effective solution to the limitations of a single GPU or node in handling massive model parameters and datasets. By distributing training tasks across multiple nodes, distributed model training enables parallel utilization of computational resources, significantly improving training efficiency. However, efficient scheduling of multi-resources (such as computation, storage, and network) in distributed environments remains a critical challenge. This paper proposes an optimized multi-resource scheduling strategy that integrates Kubernetes’ scheduling principles. By improving the scheduling algorithm, the strategy effectively enhances resource utilization and task completion efficiency. Experimental results demonstrate that the proposed method significantly reduces task waiting time and improves overall system throughput in distributed environments.

关键词： Training Job shop scheduling Fluctuations computational modeling Graphics processing units Dynamic scheduling Load management Throughput computational efficiency Resource management

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Dynamically Switching Human Prediction Models for Efficient Planning

Dynamically Switching Human Prediction Models for Efficient ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Sripathy, Arjun Bobu, Andreea Brown, Daniel S. Dragan, Anca D. Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9781728190778

As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously;a more complex one might predict that the human will act optimally, disregarding the robot;whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.

关键词： automation computational modeling Conferences Switches Predictive models Performance gain Planning

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Estimation of External Force acting on Underwater Robots

Estimation of External Force acting on Underwater Robots

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IEEE international Conference on automation Science and Engineering (CASE)

作者： Srinivasan Lakshminarayanan Daniel Duecker Andriy Sarabakha Amartya Ganguly Leila Takayama Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) TU Munich Germany Department of Electrical and Computer Engineering Aarhus University Denmark Department of Computational Media University of California USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the context of robotics, accurately estimating external forces is essential. These estimations play a crucial role in collision detection, motion planning, robot control, disturbance compensation, environmental interactions, etc. Given the importance of external force estimation, We introduce a framework to estimate the forces acting on underwater robots. By leveraging dynamic models of the underwater remotely operated vehicle (ROV), our framework predicts and estimates forces based on velocity and acceleration data. We conducted real-world experiments on BlueROV2 underwater vehicle to test and verify the efficacy of our proposed framework. We determined that our framework can predict the external forces with an MAE of 0.8 N.

关键词： Underwater structures Autonomous underwater vehicles Force measurement Remotely guided vehicles Force Robot control Estimation Mathematical models Vehicle dynamics Collision avoidance

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A robotic arm RRT* path planning algorithm based on adaptive target point sampling and path smoothing optimization

A robotic arm RRT* path planning algorithm based on adaptive...

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Artificial intelligence and Intelligent Manufacturing (AIIM), international symposium on

作者： Ze Cui Rongyang Gu Zixiang Xiong Qishuang Yang School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

Path planning is essential for robotic arms to perform tasks in complex environments, where efficiency, feasibility, and path quality are critical. This paper proposes an improved RRT* (Rapidly-exploring Random Tree Star) algorithm to address the slow convergence and poor path quality of traditional RRT*. An adaptive target sampling strategy dynamically adjusts the sampling probability, balancing global exploration and local convergence. Path optimization combines neighborhood search, optimal parent selection, and rewiring to reduce costs and improve quality. Quintic B-spline interpolation ensures smooth, continuous paths. The algorithm integrates inverse kinematics and joint space validity checks to meet kinematic constraints and practical execution. Simulations with a six-DOF robotic arm show the improved RRT* outperforms the traditional algorithm in path length, smoothness, and efficiency, especially in complex environments. This approach offers an effective solution for robotic arm planning in industrial automation, medical robotics, and similar fields.

关键词： Smoothing methods Costs Service robots Heuristic algorithms Kinematics Manipulators Path planning Planning Robots Optimization

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A novel method for computing the 3D friction cone using complimentary constraints

A novel method for computing the 3D friction cone using comp...

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IEEE international Conference on robotics and automation (ICRA)

作者： Pretorius, Dean Fisher, Callen Stellenbosch Univ Dept Elect Engn Stellenbosch South Africa Univ Stellenbosch Dept Elect & Elect Engn ZA-7599 Stellenbosch South Africa

ISBN: (纸本)9781728190778

Modeling the Coulomb Friction Cone in trajectory optimization is typically done by linearizing it along a series of vectors. Often, these vectors define the edges of polyhedral estimations of the cone. This article provides an alternate approach that samples the cone along a vector that satisfies the Maximum Dissipation Principle, which is shown to be significantly more computationally tractable. The proposed technique uses the polar representation of the relative velocity of a contact point on a surface to determine the direction of the resultant friction force and linearizes the friction cone along this vector. This study describes the development of the proposed model. Thereafter, a trajectory optimization experiment was conducted to compare the traditional four-sided polyhedral estimation of the friction cone with the novel method. Compute time and computational complexity were used as performance metrics in this study. Results from these experiments indicate that the proposed method reduced the compute time by 39.13% and a 57.00% reduction in inequality constraints when compared to the 4-sided polyhedral estimation.

关键词： Measurement Three-dimensional displays automation Friction Conferences computational modeling Force

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CCFD-SSL: Optimizing Real-Time Credit Card Fraud Detection Using Self-Supervised Learning and Contrastive Representations

CCFD-SSL: Optimizing Real-Time Credit Card Fraud Detection U...

引用

robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international Conference on

作者： Md Al Rafi Gourab Nicholas Rodrigues Md Nazmul Hossain Mir M D Shahriar Mahmud Bhuiyan Arifa Akter Eva Aminun Nahar Kamruddin Nur Washington University of Science and Technology Alexandria USA American International University - Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Credit card fraud poses an ongoing challenge for financial entities, especially within real-time transaction systems. This paper presents an innovative application of Self-Supervised Learning (SSL) aimed at tackling the difficulties associated with highly skewed credit card fraud detection (CCFD) datasets. Using transaction data from European cardholders, our SSL-based method improves the model’s ability to differentiate between fraudulent and legitimate transactions, with minimal reliance on extensive labeled datasets. The approach utilizes contrastive learning to extract useful representations of transaction data, followed by fine-tuning with labeled samples. The model delivers state-of-the-art results, including 93.7% precision, 92.3% recall, an F1-score of 92.99%, and an AUPRC of 92.45%. These out-comes underscore the effectiveness of SSL in enhancing detection rates while reducing false positives in real-time environments. Additionally, we explore the scalability and adaptability of SSL-based frameworks in responding to evolving fraud patterns, highlighting their potential to bolster financial security.

关键词： Adaptation models Sensitivity Scalability computational modeling Contrastive learning Credit cards Real-time systems Data models Fraud Tuning

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Research on Image Depth Estimation Based on PatchMatchNet in the Field of Holographic 3D Display

Research on Image Depth Estimation Based on PatchMatchNet in...

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Artificial intelligence and Intelligent Manufacturing (AIIM), international symposium on

作者： Huifeng Hu Jicheng Liu Hongyue Gao Xinglin Wang Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University hanghai China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

The display effect of real objects in holographic 3D displays primarily depends on the accuracy of their depth and detail information. However, existing methods face challenges such as insufficient precision, high computational cost, and limitations on mobile devices. To address these issues, this paper proposes a multi-view depth estimation method based on PatchMatchNet(PMN). The method extracts image features using a feature extraction network and optimizes depth estimation through an adaptive propagation and evaluation mechanism, thereby enhancing the accuracy of object depth estimation. The obtained depth map is used to generate a hologram via tomography, which is then displayed as a holographic 3D image using a Spatial Light Modulator (SLM). Comparative analysis with openMVS and MVSNet demonstrates that PMN outperforms in terms of both depth estimation accuracy and computational efficiency, validating its practical application in holographic displays.

关键词： Deep learning Visualization Three-dimensional displays Accuracy Depth measurement Modulation Tomography Feature extraction Mobile handsets computational efficiency

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