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IEEE International Conference on robotics and Automation (ICRA)

作者： Weiming Zhi Iretiayo Akinola Karl Van Wyk Nathan D. Ratliff Fabio Ramos NVIDIA USA School of Computer Science the University of Sydney Australia Robotics Institute Carnegie Mellon University Pittsburgh USA

Motion generation seeks to produce safe and feasible robot motion from start to goal. Various tools at different levels of granularity have been developed. On one extreme, sampling-based motion planners focus on completeness - a solution, if it exists, would eventually be found. However, produced paths are often of low quality, and contain superfluous motion. On the other, reactive methods optimise the immediate cost to obtain the next controls, producing smooth and legible motion that can quickly adapt to perturbations, uncertainties, and changes in the environment. However, reactive methods are highly local, and often produce motion that become trapped in non-convex regions of the environment. This paper contributes, Geometric Fabric Command Sequences, a method that lies in the middle ground. It can produce globally optimal motion that is smooth and intuitive, while being also reactive. We model motion via a reactive Geometric Fabric policy that ingests a sequence of attractor states, or commands, and then apply global optimisation over the space of commands. We postulate that solutions for different problems and scenes are highly transferable when conditioned on environmental features. Therefore, an implicit generative model is trained on solutions from optimisation and environment features in a self-supervised manner. That is, faced with multiple motion generation problems, the learning and optimisation are contained within the same loop: the optimisation generates labels for learning, while the learning improves the optimisation for the next problem, which in turn provides higher quality labels. We empirically validate our method in both simulation and on a real-world 6-DOF JACO arm.

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

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SMART: Advancing Scalable Map Priors for Driving Topology Reasoning

arXiv

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arXiv 2025年

作者： Ye, Junjie Paz, David Zhang, Hengyuan Guo, Yuliang Huang, Xinyu Christensen, Henrik I. Wang, Yue Ren, Liu Thomas Lord Department of Computer Science University of Southern California United States United States Contextual Robotics Institute UC San Diego United States

Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceiving driving topology using vehicle-mounted sensors, their scalability is hindered by the reliance on training data captured by consistent sensor configurations. We identify that the key factor in scalable lane perception and topology reasoning is the elimination of this sensor-dependent feature. To address this, we propose SMART, a scalable solution that leverages easily available standard-definition (SD) and satellite maps to learn a map prior model, supervised by large-scale geo-referenced high-definition (HD) maps independent of sensor settings. Attributed to scaled training, SMART alone achieves superior offline lane topology understanding using only SD and satellite inputs. Extensive experiments further demonstrate that SMART can be seamlessly integrated into any online topology reasoning methods, yielding significant improvements of up to 28% on the OpenLane-V2 benchmark. Project page: https://***/smart. © 2025, CC BY.

关键词： Topology

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Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Liu, Kaibo Zheng, Wangli Min, Huasong Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China State Grid Electric Power Research Institute Jiangsu Nanjing211106 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Hubei Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method;the other is an application platform experiment, which verifies the feasibility and availability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter 13

A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectr...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Hanson, Nathaniel Demirkaya, Ahmet Erdogmus, Deniz Stubbins, Aron Padir, Taskin Imbiriba, Tales Northeastern University Institute for Experiential Robotics BostonMA United States Northeastern University Marine Science Center BostonMA United States Northeastern University Department of Electrical and Computer Engineering BostonMA United States Northeastern University Institute for Experiential Ai BostonMA United States

ISBN: (纸本)9798350395570

Plastic waste entering the riverine harms local ecosystems, leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automated monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies coupled with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://***/RIVeR-Lab/hyperspectral_macro_plastic_detection © 2023 IEEE.

关键词： Hyperspectral imaging

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WildGaussians: 3D Gaussian Splatting in the Wild 38

WildGaussians: 3D Gaussian Splatting in the Wild

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kulhanek, Jonas Peng, Songyou Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science ETH Zurich Switzerland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Robot Coordination and Layout Design for Automated Warehousing

arXiv

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arXiv 2023年

作者： Zhang, Yulun Fontaine, Matthew C. Bhatt, Varun Nikolaidis, Stefanos Li, Jiaoyang Robotics Institute Carnegie Mellon University United States Department of Computer Science University of Southern California United States

With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. We extend existing automatic scenario generation methods to optimize warehouse layouts. Results show that our optimized warehouse layouts (1) reduce traffic congestion and thus improve throughput, (2) improve the scalability of the automated warehouses by doubling the number of robots in some cases, and (3) are capable of generating layouts with user-specified diversity measures. We include the source code at: https://***/lunjohnzhang/ warehouse env gen public Copyright © 2023, The Authors. All rights reserved.

关键词： Warehouses

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Optimal Energy Storage Operation under Demand Uncertainty: A Prospect Theory Analysis 14

Optimal Energy Storage Operation under Demand Uncertainty: A...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Huang, Qisheng Xu, Jin Sun, Peng Liu, Bo Courcoubetis, Costas Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Management Wuhan China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Chinese University of Hong Kong Shenzhen School of Data Science Shenzhen518712 China

ISBN: (纸本)9781665455541

In this paper, we study the consumer's optimal energy storage operation problem under demand uncertainty. Each consumer can purchase energy storage service from an independent energy storage aggregator to shift demand from peak periods to off-peak periods under time-of-use (ToU) pricing. Previous studies on energy storage operation and investment mainly focused on the expected cost-minimization problem of a risk-neutral consumer based on the expected utility theory (EUT). We propose a prospect theory (PT) model from behavioral economics to understand the consumer's realistic energy storage operation behaviors considering their risk preferences. Although it is challenging to solve the PT-based non-convex optimization problem, we are able to characterize the optimal solution by exploiting the unimodal structure of the objective function. Theoretical and numerical results show that the consumer's risk preferences have a significant impact on his decisions: 1) a PT-consumer with a low reference point is more willing to use energy storage to reduce risk compared with the EUT benchmark;and 2) a PT consumer is more willing to use the energy storage when the probability of high demand is small, due to the probability distortion. © 2023 IEEE.

关键词： Energy storage

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Generating Force Vectors from Projective Truncated Signed Distance Fields for Collision Avoidance and Haptic Feedback

Generating Force Vectors from Projective Truncated Signed Di...

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

作者： Seongjin Bien Abdeldjallil Naceri Luis Figueredo Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) Technische Universität München (TUM) Germany School of Computer Science University of Nottingham UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Signed Distance Fields are a common surface representation method widely used for both 3D mapping and obstacle avoidance. While the former traditionally uses projective Truncated Signed Distance Fields (TSDF), the latter often requires a complete Euclidean Signed Distance Field (ESDF) representation of the environment. In this paper, we propose a unified system by combining both methods to generate force vectors to nearby obstacles from a TSDF-based 3D reconstruction. We introduce a new merging scheme to better capture the geometry of the object, with no post-processing requirements, and a way to increase the effective range of the system. Validation experiments demonstrate the accuracy of the force vector calculation by comparing it against an ideal simulated environment. The flexibility of the system is demonstrated by implementing a haptic feedback teleoperation setup, which is validated through a user study in a teleoperation task. Through this, it is shown that the proposed method provides a statistically significant improvement to the task. Finally, a brief description on future improvements to the system is presented.

关键词： Three-dimensional displays Accuracy Statistical analysis Force Merging Robot vision systems Cameras Vectors Haptic interfaces Collision avoidance

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