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arXiv 2024年

作者： Biggs, Benjamin Stilwell, Daniel J. Yetkin, Harun McMahon, James US Naval Research Laboratory WashingtonDC7130 United States Bradley Department of Electrical and Computer Engineering Virginia Tech Blacks-burg VA United States Bartin University Turkey

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources. © 2024, CC BY-NC-SA.

关键词： Autonomous underwater vehicles

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Learning Personalised Models for Automatic Self-Reported Personality Recognition

Learning Personalised Models for Automatic Self-Reported Per...

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ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on computer Vision, ICCV 2021

作者： Salam, Hanan Manoranjan, Viswonathan Jiang, Jian Celiktutan, Oya SMART Laboratory Department of Computer Science New York University Abu Dhabi United Arab Emirates SAIR Laboratory Centre for Robotics Research Department of Engineering King’s College London United Kingdom

Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset. © 2022 H. Salam, V. Manoranjan, J. Jiang & O. Celiktutan.

关键词： User profile

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

arXiv

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arXiv 2024年

作者： Tang, Jiawei Zhong, Yuxing Wang, Pengyu Chen, Xingzhou Wu, Shuang Shi, Ling The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Noah’s Ark Lab Huawei Canada

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches. © 2024, CC BY.

关键词： Nonlinear programming

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Underwater Propagation of Spatially Partially Coherent Beams that Carry Orbital Angular Momentum 15

Underwater Propagation of Spatially Partially Coherent Beams...

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Ocean Sensing and Monitoring XV 2023

作者： Akhtar, Fjordia Avramov-Zamurovic, Svetlana Nelson, Charles Ferlic, Nate A. Judd, K. Peter Electrical and Computer Engineering Department United States Naval Academy 597 McNair Rd. Hopper Hall AnnapolisMD21402 United States Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd. Hopper Hall AnnapolisMD21402 United States 22347 Cedar Point Road Patuxent RiverMD20670 United States United States Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC20375 United States

ISBN: (纸本)9781510662025

We consider the design and generation of spatially partially coherent (SPC) beams carrying orbital angular momentum (OAM) propagating through complex random media. It has been theoretically shown that spatial coherence can be controlled through a prescribed linear superposition of Laguerre-Gaussian (LG) modes. Experimentally the SPC beams are obtained by randomly cycling the phase screens of the coherent modes, with each mode contributing a weight that is proportional to its eigenvalue in the coherent mode decomposition equation. The spectral degree of coherence, ξ, theoretically varies from 0 (fully coherent) to 1 (incoherent). Experimentally, it is suggested that we can reach the highest level of incoherence when the modes are combined where LG mode orders are of equal weights. Preliminary measurements indicate a reduced coherence corresponding to increasing ξ. Our experimental design imposes turbulence on the beam to examine the effects of its spatial partial coherence on the scintillation index (SI). It has been shown that benefits to communication system performance, specifically underwater, can be achieved through the control of spatial coherence properties of laser light propagation. © 2023 SPIE.

关键词： Turbulence

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1817-1826页

作者： Gaofeng Li Dezhen Song Lei Sun Shan Xu Hongpeng Wang Jingtai Liu Nankai University Tianjin 300350China Humanoids and Human Centered Mechatronics Research Line Italian Institute of Technology16163 GenovaItaly Department of Computer Science and Engineering Texas A&M UniversityCollege StationTexas 77843 USA Institute of Robotics and Automatic Information System Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable *** deformable link is composed of passive spherical joints with preload forces between socket-ball *** estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable *** yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction *** further develop a parameter estimation method to estimate the product of preload force and static friction *** experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

关键词： Deformable link parameters identification spherical joint with preload force static force-based model

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Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Toward Fine Contact Interactions: Learning to Control Normal...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jinda Cui Jiawei Xu David Saldana Jeff Trinkle Lehigh AIRLab Honda Research Institute USA San Jose CA USA Department of Computer Science and Engineering Autonomous and Intelligent Robotics Laboratory (AIRLab) Lehigh University Bethleham PA USA

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, i.e., controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

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Applying Machine Learning in Designing Distributed Auction for Multi-agent Task Allocation with Budget Constraints 20

Applying Machine Learning in Designing Distributed Auction f...

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20th International Conference on Advanced robotics, ICAR 2021

作者： Luo, Chen Huang, Qinwei Kong, Fanxin Khan, Simon Qiu, Qinru Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY United States Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9781665436847

The multi-agent task allocation can be solved in a distributed manner using Consensus-Based Bundle Algorithm (CBBA). Under this distributed auction process, each agent greedily maximizes the global score, which is the difference of the reward and the cost, through an iterative bundle construction and conflict resolution procedure. The distributed algorithm has provable convergence and guarantees 50% optimality if the score function satisfies the condition of diminishing marginal gain (DMG). While the previous work focuses on unconstrained optimization of rewards, this paper aims at applying CBBA to task allocation with budget constraints. Several heuristics were proposed to build the bundle and calculate the bidding scores as improvements to the original CBBA algorithm. We then prove that some of the new score functions are DMG, and therefore guarantees the convergence of the distributed process. We also show that these heuristic extended CBBAs are Pareto efficient;using different heuristic extensions under different scenarios is more efficient than consistently using the same one. To decide which heuristic extension should be used for a given task allocation problem, a graph convolutional neural network (GCN) model is trained to extract and analyze the features of the constrained optimization problem as a graph, and predict the potential performance (i.e., global reward) of different heuristic extensions. Based on the prediction, the best heuristic extension will be selected. Experimental results show that the predicted reward has more than 0.98 correlation with the actual reward and for 70% of time the prediction guided selection picks the best heuristic extension for the budget constrained task allocation problem. © 2021 IEEE.

关键词： Iterative methods

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Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

Learning Perceptual Hallucination for Multi-Robot Navigation...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jin-Soo Park Xuesu Xiao Garrett Warnell Harel Yedidsion Peter Stone Department of Electrical and Computer Engineering The University of Texas at Austin United States Department of Computer Science George Mason University United States Everyday Robots United States Department of Computer Science The University of Texas at Austin United States Army Research Laboratory United States Sony AI Japan

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other in a narrow hallway, they may either turn around to find an alternative route or collide with each other. This paper presents a new approach to navigation that allows two robots to pass each other in a narrow hallway without colliding, stopping, or waiting. Our approach, Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically generate virtual obstacles (i.e., perceptual hallucination) to facilitate passing in narrow hallways by multiple robots that utilize otherwise standard autonomous navigation systems. Our experiments on physical robots in a variety of hallways show improved performance compared to multiple baselines.

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