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arXiv 2023年

作者： Liu, Xuan Onal, Cagdas D. Fu, Jie The Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

This paper aims to solve the contact-aware locomotion problem of a soft snake robot by developing bio-inspired contact-aware locomotion controllers. To provide effective contact information for the controllers, we develop a scale-covered sensor structure mimicking natural snakes' scale sensilla. In the design of the control framework, our core contribution is the development of a novel sensory feedback mechanism for the Matsuoka central pattern generator (CPG) network. This mechanism allows the Matsuoka CPG system to work like a "spinal cord" in the whole contact-aware control scheme, which simultaneously takes the stimuli including tonic input signals from the "brain" (a goal-tracking locomotion controller) and sensory feedback signals from the "reflex arc" (the contact reactive controller), and generates rhythmic signals to actuate the soft snake robot to slither through densely allocated obstacles. In the "reflex arc" design, we develop two distinctive types of reactive controllers - 1) a reinforcement learning (RL) sensor regulator that learns to manipulate the sensory feedback inputs of the CPG system, and 2) a local reflexive sensor-CPG network that directly connects sensor readings and the CPG's feedback inputs in a specific topology. Combining with the locomotion controller and the Matsuoka CPG system, these two reactive controllers facilitate two different contact-aware locomotion control schemes. The two control schemes are tested and evaluated in both simulated and real soft snake robots, showing promising performance in the contact-aware locomotion tasks. The experimental results also validate the advantages of the modified Matsuoka CPG system with a new sensory feedback mechanism for bio-inspired robot controller design. © 2023, CC BY.

关键词： Controllers

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Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs

arXiv

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arXiv 2023年

作者： Qian, Zhentian Fu, Jie Xiao, Jing The Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This paper introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results. © 2023, CC BY.

关键词： Semantics

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A Language Anchor-Guided Method for Robust Noisy Domain Generalization

arXiv

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arXiv 2025年

作者： Dai, Zilin Wang, Lehong Lin, Fangzhou Wang, Yidong Li, Zhigang Yamada, Kazunori D. Zhang, Ziming Lu, Wang Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Carnegie Mellon University PittsburghPA15213 United States Tohoku University Sendai980-8572 Japan Tsinghua University Beijing100190 China Peking University Beijing100871 China

Real-world machine learning applications are often hindered by two critical challenges: distribution shift and label noise. Networks inherently tend to overfit to redundant, uninformative features present in the training distribution, which undermines their ability to generalize effectively to the target domain’s distribution. The presence of noisy data further exacerbates this issue by inducing additional overfitting to noise, causing existing domain generalization methods to fail in effectively distinguishing invariant features from spurious ones. To address these challenges, we propose Anchor Alignment and Adaptive Weighting (A3W), a novel algorithm based on sample reweighting guided by natural language processing (NLP) anchors that seeks to extract representative features. In particular, A3W leverages semantic representations derived from natural language models to serve as a source of domain-invariant prior knowledge. We also introduce a weighted loss function that dynamically adjusts the contribution of each sample based on its distance to the corresponding NLP anchor, thereby improving the model’s resilience to noisy labels. Extensive experiments on benchmark datasets demonstrate that A3W outperforms state-of-the-art domain generalization methods, yielding significant improvements in both accuracy and robustness across various datasets and noise levels. © 2025, CC BY.

关键词： Adversarial machine learning

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Multi-Robot Scan-n-Print for Wire Arc Additive Manufacturing

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ASME Letters in Translational Robotics 2025年第1期1卷 011003页

作者： Lu, Chen-Lung He, Honglu Ren, Jinhan Dhar, Joni Saunders, Glenn Julius, Agung Samuel, Johnson Wen, John T. https://***/01rtyzb94Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 https://***/01rtyzb94Rensselaer Polytechnic Institute Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY 12180 https://***/01rtyzb94Rensselaer Polytechnic Institute Manufacturing Innovation Center Rensselaer Polytechnic Institute Troy NY 12180 Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY 12180

Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology offering flexible 3D printing while ensuring high-quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metals such as aluminum alloys. In this article, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-DoF welding robot, a 2-DoF trunnion platform, and a 6-DoF sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to changes in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed-loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.

关键词： welding process control multiple robot scan-n-print welding robot WAAM

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Pilot-wave dynamics: Using dynamic mode decomposition to characterize bifurcations, routes to chaos, and emergent statistics

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Physical Review E 2023年第3期108卷 034213-034213页

作者： J. Nathan Kutz André Nachbin Peter J. Baddoo John W. M. Bush Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester Massachusetts 01742 USA Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

We develop a data-driven characterization of the pilot-wave hydrodynamic system in which a bouncing droplet self-propels along the surface of a vibrating bath. We consider drop motion in a confined one-dimensional geometry and apply the dynamic mode decomposition (DMD) in order to characterize the evolution of the wave field as the bath's vibrational acceleration is increased progressively. Dynamic mode decomposition provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of spatiotemporal data. Thus, DMD reduces the complex nonlinear interactions between pilot waves and droplet to a low-dimensional linear superposition of DMD modes characterizing the wave field. In particular, it provides a low-dimensional characterization of the bifurcation structure of the pilot-wave physics, wherein the excitation and recruitment of additional modes in the linear superposition models the bifurcation sequence. This DMD characterization yields a fresh perspective on the bouncing-droplet problem that forges valuable new links with the mathematical machinery of quantum mechanics. Specifically, the analysis shows that as the vibrational acceleration is increased, the pilot-wave field undergoes a series of Hopf bifurcations that ultimately lead to a chaotic wave field. The established relation between the mean pilot-wave field and the droplet statistics allows us to characterize the evolution of the emergent statistics with increased vibrational forcing from the evolution of the pilot-wave field. We thus develop a numerical framework with the same basic structure as quantum mechanics, specifically a wave theory that predicts particle statistics.

关键词： Dynamical systems

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A Single-Cell-Resolution Quantitative Metric of Similarity to a Target Cell Type for scRNA-seq Data

A Single-Cell-Resolution Quantitative Metric of Similarity t...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Cheng, Zuolin Wei, Songtao Yu, Guoqiang Virginia Polytechnic Institute and State University The Department of Electrical and Computer Engineering ArlingtonVA United States

ISBN: (纸本)9781665468190

Empowered by advances in single-cell RNA sequencing techniques (scRNA-seq), discovering new cell types or new subsets of a cell type has become an increasingly popular research interest. This type of study, by nature, requires assessment of similarity between cell groups. However, so far there is no quantitative metric for accurate and objective evaluation of such similarity;while current practice suffers from quite a few challenges including subjectivity. In this work, we propose a novel quantitative metric of single-cell-to-target-cell-type similarity, on the basis of scRNA-seq data and the signatures or differentially expressed gene (DEG) list of the target cell type. The proposed similarity score, TySim, evaluates the statistical significance of joint differential expression of the given DEGs in the cell to be tested. For this statistical test, the null distribution is established upon full consideration of complex factors causing heterogeneous sequencing efficiency of genes/cells. The design of TySim avoids the needs for clustering and for batch effect removal on cross-platform data, detouring the accompanying risks and burdens. Being the first quantitative metric of similarity to target cell type at a single-cell resolution, TySim has the potential to facilitate and enable a variety of biological studies. We validated the effectiveness of TySim and explored the possible directions of application through three example study cases of real datasets. Experimental results demonstrate TySim's superior performance and great potential in making contributions to biological studies. © 2022 IEEE.

关键词： Genes

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Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network 30

Exploring Anatomical Similarity in Cardiac-Gated Spect Image...

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30th IEEE International Conference on Image Processing, ICIP 2023

作者： Zhang, Xirang Yang, Yongyi Pretorius, P. Hendrik King, Michael A. Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Massachusetts Medical School Department of Radiology WorcesterMA01655 United States

ISBN: (纸本)9781728198354

Motion compensation is effective for reducing motion blur in cardiac gated imaging. In this work, we investigate the potential benefit of incorporating an anatomical similarity measure in training a deep learning (DL) network for motion compensation on cardiac gated SPECT images, which are known to suffer from limited data counts and exhibit image intensity distortion (due to partial-volume effect) associated with cardiac motion. In this similarity measure we utilize the spatial image gradient to characterize the correspondence of boundary points on the left-ventricular wall between two gate frames. In the experiment we demonstrated this approach on a set of 197 clinical acquisitions, and the results show that with the proposed approach the DL network can improve the anatomical similarity among the gate frames upon motion compensation. © 2023 IEEE.

关键词： Motion compensation

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Development of a Dynamic Model of Small Autonomous Hydrofoil Craft

Development of a Dynamic Model of Small Autonomous Hydrofoil...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Moon, Heejip Henry Steel, Gwyneth Dougherty, Hugh Stilwell, Daniel J. Brizzolara, Stefano Bradley Department of Electrical and Computer Engineering United States Virginia Polytechnic Institute and State University Kevin T. Crofton Department of Aerospace and Ocean Engineering BlacksburgVA United States

ISBN: (纸本)9798218142186

In this work, we develop a six degree of freedom nonlinear dynamic model of a single-mast fully submerged hydrofoil platform. Towing tank data is utilized to assist in characterization of the hydrofoils. The aim of the model is to aid development of various model-based controllers for autonomous operation by simulating and evaluating their performance before implementation in the field. © 2023 The Marine Technology Society (MTS).

关键词： Degrees of freedom (mechanics)

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How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? 41

How Do Nonlinear Transformers Learn and Generalize in In-Con...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Lu, Songtao Cui, Xiaodong Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding ICL capacity is mostly elusive due to the technical challenges of analyzing the nonconvex training problems resulting from the nonlinear self-attention and nonlinear activation in Transformers. To the best of our knowledge, this paper provides the first theoretical analysis of the training dynamics of Transformers with nonlinear self-attention and nonlinear MLP, together with the ICL generalization capability of the resulting model. Focusing on a group of binary classification tasks, we train Transformers using data from a subset of these tasks and quantify the impact of various factors on the ICL generalization performance on the remaining unseen tasks with and without data distribution shifts. We also analyze how different components in the learned Transformers contribute to the ICL performance. Furthermore, we provide the first theoretical analysis of how model pruning affects ICL performance and prove that proper magnitude-based pruning can have a minimal impact on ICL while reducing inference costs. These theoretical findings are justified through numerical experiments. Copyright 2024 by the author(s)

关键词： Distribution transformers

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Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection:PULSE

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Microsystems & Nanoengineering 2024年第6期10卷 173-188页

作者： Peiran Zhang Zhenhua Tian Ke Jin Kaichun Yang Wesley Collyer Joseph Rufo Neil Upreti Xianjun Dong Luke P.Lee Tony Jun Huang Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Genomics and Bioinformatics Hub Department of NeurologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Medicine Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Bioengineering Department of Electrical Engineering and Computer ScienceUniversity of California at BerkeleyBerkeleyCAUSA Institute of Quantum Biophysics Department of BiophysicsSungkyunkwan UniversitySuwonKorea Department of Chemistry&Nanoscience Ewha Womans UniversitySeoulKorea

Laboratory automation technologies have revolutionized biomedical ***,the availability of automation solutions at the single-cell level remains scarce,primarily owing to the inherent challenges of handling cells with such small dimensions in a precise,biocompatible ***,we present a single-cell-level laboratory automation solution that configures various experiments onto standardized,microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology,known as *** enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable,scalable,and biocompatible *** pipetting robots,PULSE enables researchers to conduct biological experiments using single cells as anchoring points(e.g.,1 cell vs.1000 cells per“tube”),achieving higher resolution and potentially more relevant data for modeling and downstream *** demonstrate the ability of PULSE to perform biofabrication,precision gating,and deterministic array barcoding via preallocated droplet-addressable *** cells can be gently printed at a speed range of 5–20 cell⋅s−1 with an accuracy of 90.5–97.7%,which can then adhere to the substrate and grow for up to 72 h while preserving cell *** the deterministic barcoding experiment,95.6%barcoding accuracy and 2.7%barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single *** PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level,offering researchers a powerful tool in biomedical research.

关键词： precise liquid offering

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