arXiv subjects

Andrew Nguyen

Publications and source records attributed to Andrew Nguyen.

8 recordsLinked to original sources

arXiv2026

Steerable and Reactive Grasping Through Modular Design with a Three-Point Interface

Dexterous grasping requires deciding where to grasp, reaching the target, and maintaining stable contact. We connect these stages through a compact three-point interface that separates global geometric reasoning from local contact control. Given object geometry and optional language commands, our framework samples contact triples from a precomputed grasp-affordance heatmap. A model-based reactive controller tracks the object, avoids collisions, and guides the hand toward the selected contacts. In the final centimeters, a Reinforcement Learning (RL) policy uses proprioceptive feedback to refine and stabilize the grasp despite reaching and perception errors. It observes only finger joint states and its recent actions, with no target points, visual observations, or object geometry, so a single policy is shared across objects and grasp configurations. In simulation, we compare grasp-and-lift success against squeeze and end-to-end baselines, characterize reaching convergence, and demonstrate grasp steering; hardware demonstrations on two training objects and one unseen object illustrate the full pipeline. Our modular framework uses geometry to guide the reach and local feedback to secure the grasp.

cs.RO↗

arXiv2026

A High-Payload Wall-Climbing Robot Using Passive Bistable Suction Cups

Wall-climbing robots capable of scaling vertical surfaces could help automate hazardous or labor intensive tasks such as window washing, inspection, maintenance, and construction. Active adhesion methods achieve higher payload capacities, but require power to maintain their grip. Passive adhesion devices such as suction cups are an attractive option for such robots because they do not require power to maintain their grip, but they are limited by their payload capacity. This work presents a novel high-payload wall-climbing robot that utilizes passive bistable suction cups to generate adhesion without needing to be pushed into the wall. The robot features a track-based system that automatically engages and disengages bistable suction cups to achieve locomotion on smooth surfaces. The robot is able to achieve vertical wall climbing on glass, wood, metal, and painted surfaces, sideways and upside-down climbing, and is able to tow a payload of 7.940 kg (with a payload-to-weight ratio of 2.25).

cs.RO↗

arXiv2026

Flocking with Multiple Types: Competition, Fluid Limits and Traveling Waves

We study a class of interacting particle systems on $\mathbb{R}$ with two types. Particles evolve by independent jumps sampled from a fixed distribution, with type-dependent jump rates $v_+$, $v_-$ and stochastic type switching driven by non-local order-based interactions. The switching rates depend on the empirical distribution through the proportion of opposite-type particles located ahead, leading to a nonlinear and discontinuous dependence on the empirical measure outside the standard Lipschitz McKean-Vlasov framework. Our first main result is a law of large numbers for the empirical measure process: we prove convergence, along subsequences, to a deterministic measure-valued process characterized by a McKean-Vlasov equation. The proof combines tightness in Wasserstein space with a martingale characterization of limit points. A uniqueness argument based on a Kolmogorov-Smirnov-type distance adapted to the ordering structure yields convergence of the full empirical measure sequence and, in turn, propagation of chaos on finite time intervals. We then study the long-time behavior of the limiting dynamics. Because the system has persistent drift, invariant distributions do not arise; instead, we analyze traveling waves, corresponding to stationary profiles in a moving frame. For exponential jump distributions, the associated non-local integro-differential system admits a local description. In the regime $v_+>v_-=0$, this further reduces to a coupled system of non-linear ODEs, allowing a phase-plane analysis that yields a traveling wave as a heteroclinic orbit connecting two equilibria. We also identify the wave speed and mass partition, and derive tail asymptotics by spectral analysis of the linearized system.

math.PR↗

arXiv2026

Impact of inhomogeneous curvature on growth rate measurements from magnitude fluctuations

Our interpretation of current cosmological observations rests on the assumptions of homogeneity and isotropy, leading to uniform background curvature and expansion characterised by the Friedmann-Lemaître-Robertson-Walker (FLRW) spacetime metric. However, the large-scale structure of the Universe is non-uniform in detail, inducing inhomogeneous curvature and scale factor variations. In this paper, we use numerical cosmological simulations generated in full General Relativity to study the impact of inhomogeneous spacetime on the magnitude fluctuations of distant objects, focusing on their use as a probe of the growth rate of cosmic structure. We quantify the distortions in the magnitude correlation spectrum as a function of angular scale and redshift, and use these distortions to infer the systematic offset in the growth rate measurement. We find that at $z \lesssim 0.2$, the systematic offset in growth rate measurements between the full numerical relativity and FLRW treatments is sub-dominant to the statistical error of current datasets, confirming that FLRW modelling is adequate for current low-redshift peculiar velocity experiments. Future datasets extending to higher redshift may require theoretical models that additionally incorporate the contributions of gravitational lensing and inhomogeneous curvature.

astro-ph.CO↗

arXiv2025

PyAtoms: An interactive tool for simulating atomic scanning tunneling microscopy images of 2D materials, moiré systems and superlattices

We present PyAtoms, an interactive open-source software that rapidly simulates atomic-scale scanning tunneling microscopy (STM) and other scanning probe microscopy (SPM) images of two-dimensional (2D) layered materials, moiré systems, and superlattices. Rooted in a Fourier-space description of ideal atomic lattice images, PyAtoms is a Python-based graphical user interface (GUI) with robust capabilities for tuning lattice parameters (lattice constants, strain, number of layers, twist angles) and STM imaging parameters (pixels, scan size, scan angle) and provides time estimates for spectroscopic measurements. These capabilities allow users to efficiently plan time-consuming STM experiments. We provide an overview of PyAtoms' current features, describe its underlying mathematical principles, and then demonstrate simulations of several 2D materials including graphene with variable sublattice asymmetry, twisted tri-layer graphene moiré systems, and several charge- and bond-density wave systems.

cond-mat.mes-hall↗

arXiv2024

Nonparametric tests for interaction in two-way ANOVA with balanced replications

Nonparametric procedures are more powerful for detecting interaction in two-way ANOVA when the data are non-normal. In this paper, we compute null critical values for the aligned rank-based tests (APCSSA/APCSSM) where the levels of the factors are between 2 and 6. We compare the performance of these new procedures with the ANOVA F-test for interaction, the adjusted rank transform test (ART), Conover's rank transform procedure (RT), and a rank-based ANOVA test (raov) using Monte Carlo simulations. The new procedures APCSSA/APCSSM are comparable with existing competitors in all settings. Even though there is no single dominant test in detecting interaction effects for non-normal data, nonparametric procedure APCSSM is the most highly recommended procedure for Cauchy errors settings.

stat.ME↗

arXiv2018

Pachinko Prediction: A Bayesian method for event prediction from social media data

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.

cs.CY↗

arXiv2018

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.

cs.SI↗