arXiv subjects

Beom Jun Kim

Publications and source records attributed to Beom Jun Kim.

At least 19 recordsLinked to original sources

arXiv2026

DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection

Scalable collection of dexterous manipulation demonstrations remains a major bottleneck for robot learning. High-fidelity interfaces often require costly hardware and extensive setup, while low-setup, low cost alternatives tend to provide less precise control and impose greater cognitive workload on operators. We present DexDirect, a direct kinesthetic arm guidance for efficient dexterous demonstration collection. The operator drags a 6-DoF gravity-compensated robot arm directly by a handle, while a single webcam retargets operator's other hand onto a 16 joints 13-DoF dexterous robot hand. User studies suggest DexDirect collects 17.2x and 3.2x more successful demonstrations compared to purely vision (AnyTeleop) and pose-tracking (TeleDex) baselines. An adapted NASA-TLX shows DexDirect greatly reduces mental demand, effort, and frustration, despite raising physical demand. A diffusion policy trained on DexDirect demonstrations reaches a 90% success rate on a cube pick-and-place task. These results suggest that direct kinesthetic arm guidance combined with vision-based hand retargeting provides an efficient low-setup and scalable interface for collecting dexterous manipulation demonstrations

cs.RO↗

arXiv2026

RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC

Humanoid navigation in dynamic environments requires long-horizon planning while respecting short-horizon dynamic and safety constraints. Classical visibility-graph planners combined with model predictive control (MPC) can efficiently generate collision-free trajectories, but their performance depends on manually tuned parameters and accurate system modeling. In real robotic systems, control delays, state-estimation noise, and locomotion uncertainties can cause overshoot and constraint violations even when the nominal path is geometrically optimal. We propose RAVEN, a hierarchical reinforcement learning (RL)-MPC framework for robust humanoid navigation. Unlike prior approaches that use learning to tune cost weights or replace planning entirely, RAVEN employs RL to adapt the geometric construction of a visibility-graph planner by modifying obstacle inflation and related graph parameters. By directly reshaping the free-space geometry, the learned planner alters the topology of the global path to compensate for delay and tracking imperfections. A collision-free MPC layer then tracks the planned trajectory while explicitly enforcing velocity bounds and obstacle-avoidance constraints. By training under realistic delays and observation noise, RAVEN learns planning adaptations that improve robustness while retaining explicit long-horizon geometric planning and constrained optimization, in contrast to end-to-end learning approaches. We evaluate RAVEN against a manually tuned visibility-graph MPC baseline and a pure RL navigation policy. Results demonstrate reduced overshoot near obstacles, improved robustness in narrow passages, and more reliable navigation under delay and noise. These findings indicate that reinforcement-adaptive graph construction combined with constrained MPC provides an effective and interpretable alternative to end-to-end learning for robust humanoid navigation.

cs.RO↗

arXiv2026

MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand

Dexterous manipulation is limited not only by algorithms but by a shortage of accessible hand hardware that combines human-scale morphology, ease of manufacturing or maintenance, tactile sensing, and practical cost. Existing dexterous hands tend to optimize some of these properties at the expense of others. We present MIDAS Hand, a low-cost, open-source, human-scale dexterous hand with integrated tactile sensing for manipulation research. MIDAS Hand provides 16 total degrees of freedom (DoF) with 13 active DoF, directly driven actuation with measurably low backdrive torque, and 283 three-axis tactile taxels in a compact 700 g package with a bill of materials under 3,000 USD. Built from 3D-printed components, it assembles in under three hours while providing the strength, repeatability, and maintainability needed for repeated real-world experiments. Alongside the hardware, we release a full stack: design files, build documentation, control and tactile Python APIs, simulation models, and retargeting and teleoperation pipelines. We characterize MIDAS Hand through workspace and grasp-taxonomy analysis, payload and reliability tests, backdrivability measurements, and teleoperation demonstrations with tactile sensing, showing that it offers a balanced, reproducible platform for tactile dexterous manipulation and human-to-robot data collection. Project page: https://midas-hand.com

cs.RO↗

arXiv2026

DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning

Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while embodiment mismatch between demonstration and deployment requires visual post-processing before policy training. We present DexEXO, a wearability-first hand exoskeleton that aligns visual appearance, contact geometry, and kinematics at the hardware level. DexEXO features a pose-tolerant thumb mechanism and a slider-based finger interface analytically modeled to support hand lengths from 140~mm to 217~mm, reducing operator-specific fitting and enabling scalable cross-operator data collection. A passive hand visually matches the deployed robot, allowing direct policy training from raw wrist-mounted RGB observations. User studies demonstrate improved comfort and usability compared to prior wearable systems. Using visually aligned observations alone, we train diffusion policies that achieve competitive performance while substantially simplifying the end-to-end pipeline. These results show that prioritizing wearability and hardware-level embodiment alignment reduces both human and algorithmic bottlenecks without sacrificing task performance. Project Page: https://dexexo-research.github.io/

cs.RO↗

arXiv2026

WHED: A Wearable Hand Exoskeleton for Natural, High-Quality Demonstration Collection

Scalable learning of dexterous manipulation remains bottlenecked by the difficulty of collecting natural, high-fidelity human demonstrations of multi-finger hands due to occlusion, complex hand kinematics, and contact-rich interactions. We present WHED, a wearable hand-exoskeleton system designed for in-the-wild demonstration capture, guided by two principles: wearability-first operation for extended use and a pose-tolerant, free-to-move thumb coupling that preserves natural thumb behaviors while maintaining a consistent mapping to the target robot thumb degrees of freedom. WHED integrates a linkage-driven finger interface with passive fit accommodation, a modified passive hand with robust proprioceptive sensing, and an onboard sensing/power module. We also provide an end-to-end data pipeline that synchronizes joint encoders, AR-based end-effector pose, and wrist-mounted visual observations, and supports post-processing for time alignment and replay. We demonstrate feasibility on representative grasping and manipulation sequences spanning precision pinch and full-hand enclosure grasps, and show qualitative consistency between collected demonstrations and replayed executions.

cs.RO↗

arXiv2024

Opinion dynamics model of collaborative learning

We propose a simple model to explore an educational phenomenon where the correct answer emerges from group discussion. We construct our model based on several plausible assumptions: (i) We tend to follow peers' opinions. However, if a peer's opinion is too different from yours, you are not much influenced. In other words, your opinion tends to align with peers' opinions, weighted by the similarity to yours. (ii) Discussion among group members helps the opinion to shift toward the correct answer even when the group members do not know it clearly. However, if everyone tells exactly the same, you often get lost and it becomes more difficult to find the correct answer. In other words, you can find the correct answer when everyone has largely different voices. (iii) We are sometimes stuck to our past. If you keep one opinion for a long time, such a memory works like an inertia in classical mechanics. We use our model to perform numerical investigations and find that the performance of a group is enhanced when initial opinions are diverse, that a lower memory capacity makes consensus occur faster, and that a small group size, typically three or four, is beneficial for better group performance.

physics.soc-ph↗

arXiv2024

Network analysis reveals news press landscape and asymmetric user polarization

Unlike traditional media, online news platforms allow users to consume content that suits their tastes and to facilitate interactions with other people. However, as more personalized consumption of information and interaction with like-minded users increase, ideological bias can inadvertently increase and contribute to the formation of echo chambers, reinforcing the polarization of opinions. Although the structural characteristics of polarization among different ideological groups in online spaces have been extensively studied, research into how these groups emotionally interact with each other has not been as thoroughly explored. From this perspective, we investigate both structural and affective polarization between news media user groups on Naver News, South Korea's largest online news portal, during the period of 2022 Korean presidential election. By utilizing the dataset comprising 333,014 articles and over 36 million user comments, we uncover two distinct groups of users characterized by opposing political leanings and reveal significant bias and polarization among them. Additionally, we reveal the existence of echo chambers within co-commenting networks and investigate the asymmetric affective interaction patterns between the two polarized groups. Classification task of news media articles based on the distinct comment response patterns support the notion that different political groups may employ distinct communication strategies. Our approach based on network analysis on large-scale comment dataset offers novel insights into characteristics of user polarization in the online news platforms and the nuanced interaction nature between user groups.

cs.SI↗

arXiv2022

Multiresolution community analysis of international trade networks

The international trade network is a complex system where multiple trade blocs with varying sizes coexist and overlap with each other. However, the resulting structures of community detection in trade networks are often inconsistent and fails to capture the complex landscape of international trade. To address these problems, we propose a multiresolution framework that aggregates all the configuration information from a range of resolutions. This allows us to consider trade communities of different sizes and illuminate the underlying hierarchical structure of trade networks and its constituting blocks. Furthermore, by measuring membership inconsistency (MeI) of each country and conducting multiple regression analysis with various economic and political indicators, we demonstrate that there exists a positive correlation between the external instability of countries and their structural inconsistency in terms of network topology.

physics.soc-ph↗

arXiv2021

Power-grid stability predictions using transferable machine learning

Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms -- random forest, support vector machine, and artificial neural network -- training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.

physics.soc-ph↗

arXiv2021

Self-consistent gravity model for inferring node mass in flow networks

The gravity model, inspired by Newton's law of universal gravitation, has long served as a primary tool for interpreting trade flows between countries, using a country's economic `mass' as a key determinant. Despite its wide application, the definition of `mass' within this model remains ambiguous. It is often approximated using indicators like GDP, which may not accurately reflect a country's true trade potential. Here, we introduce a data-driven, self-consistent numerical approach that redefines `mass' from a static proxy to a dynamic attribute inferred directly from flow data. We infer mass distribution and interaction nature through our method, mirroring Newton's approach to understanding gravity. Our methodology accurately identifies predefined embeddings and reconstructs system attributes when applied to synthetic flow data, demonstrating its strong predictive power and adaptability. Further application to real-world trade networks yields critical insights, revealing the spatial spectrum of trade flows and the economic mass of countries, two key features unexplored in depth by existing models. Our methodology not only enables accurate reconstruction of the original flow but also allows for a deep understanding of the unique capabilities of each node within the network. This study marks a significant shift in the understanding and application of the gravity model, providing a more comprehensive tool for analyzing complex systems and uncovering new insights into various fields, including global trade, traffic engineering, epidemic disease prevention, and infrastructure design.

physics.data-an↗

arXiv2021

Consistency landscape of network communities

The concept of community detection has long been used as a key device for handling the mesoscale structures in networks. Suitably conducted community detection reveals various embedded informative substructures of network topology. However, regarding the practical usage of community detection, it has always been a tricky problem to assign a reasonable community resolution for networks of interest. Because of the absence of the unanimously accepted criterion, most of the previous studies utilized rather ad hoc heuristics to decide the community resolution. In this work, we harness the concept of consistency in community structures of networks to provide the overall community resolution landscape of networks, which we eventually take to quantify the reliability of detected communities for a given resolution parameter. More precisely, we exploit the ambiguity in the results of stochastic detection algorithms and suggest a method that denotes the relative validity of community structures in regard to their stability of global and local inconsistency measures using multiple detection processes. Applying our framework to synthetic and real networks, we confirm that it effectively displays insightful fundamental aspects of community structures.

physics.soc-ph↗

arXiv2021

Different Environmental Conditions in Genetic Algorithm

We propose an extended genetic algorithm (GA) with different local environmental conditions. Genetic entities, or configurations, are put on nodes in a ring structure, and location-dependent environmental conditions are applied for each entity. Our GA is motivated by the geographic aspect of natural evolution: Geographic isolation reduces the diversity in a local group, but at the same time, can enhance intergroup diversity. Mating of genetic entities across different environments can make it possible to search for broad area of the fitness landscape. We validate our extended GA for finding the ground state of three-dimensional spin-glass system and find that the use of different environmental conditions makes it possible to find the lower-energy spin configurations at relatively shorter computation time. Our extension of GA belongs to a meta-optimization method and thus can be applied for a broad research area in which finding of the optimal state in a shorter computation time is the key problem.

physics.data-an↗

arXiv2021

Impact of personal income on mortality by age: biological versus socio-economic effects

The influence of per capita income on life expectancy is well documented, mostly through studies of multinational samples. However, one expects fairly weak correlations at both ends of the life span, that is to say in early infancy and in age groups of elderly from 85 to 100 years. The reason is that at both ends mortality is largely controled by biological factors rather than by socio-economic conditions. In order to test this conjecture, we explore the influence of income on age groups, separately in France, the United States and South Korea. More precisely in each country we compare income and mortality data in as many regional subunits as possible. One noteworthy constatation is that, contrary to a common view, personal income is only weakly correlated with infant mortality (i.e. mortality under the age of one year). More broadly, we propose as a conjecture that the common pattern revealed by the analysis of the three countries is also valid in other developed countries.

physics.soc-ph↗

arXiv2020

Co-sponsorship analysis of party politics in the 20th National Assembly of Republic of Korea

We investigate co-sponsorship among lawmakers by applying the principal-component analysis to the bills introduced in the 20th National Assembly of Korea. The most relevant factor for co-sponsorship is their party membership, and we clearly observe a signal of a third-party system in action. To identify other factors than the party influence, we analyze how lawmakers are clustered inside each party, and the result shows significant similarity between their committee membership and co-sponsorship in case of the ruling party. In addition, by monitoring each lawmaker's similarity to the average behavior of his or her party, we have found that it begins to decrease approximately one month before the lawmaker actually changes the party membership.

physics.soc-ph↗

arXiv2020

Extracting hierarchical backbones from bipartite networks

We propose a method for extracting hierarchical backbones from a bipartite network. Our method leverages the observation that a hierarchical relationship between two nodes in a bipartite network is often manifested as an asymmetry in the conditional probability of observing the connections to them from the other node set. Our method estimates both the importance and direction of the hierarchical relationship between a pair of nodes, thereby providing a flexible way to identify the essential part of the networks. Using semi-synthetic benchmarks, we show that our method outperforms existing methods at identifying planted hierarchy while offering more flexibility. Application of our method to empirical datasets---a bipartite network of skills and individuals as well as the network between gene products and Gene Ontology (GO) terms---demonstrates the possibility of automatically extracting or augmenting ontology from data.

cs.SI↗

arXiv2019

Scaling relations and finite-size scaling in gravitationally correlated lattice percolation models

In some systems, the connecting probability (and thus the percolation process) between two sites depends on the geometric distance between them. To understand such process, we propose gravitationally correlated percolation models for link-adding networks on the two-dimensional lattice $G$ with two strategies $S_{\rm max}$ and $S_{\rm min}$, to add a link $l_{i,j}$ to connect site $i$ and site $j$ with mass $m_i$ and $m_j$, respectively; $m_i$ and $m_j$ are sizes of the clusters which contain site $i$ and site $j$, respectively. The probability to add the link $l_{i,j}$ is related to the generalized gravity $g_{ij} \equiv m_i m_j/r_{ij}^d$, where $r_{ij}$ is the geometric distance between $i$ and $j$, and $d$ is an adjustable decaying exponent. In the beginning of the simulation, all sites of $G$ are occupied and there is no link. In the simulation process, two inter-cluster links $l_{i,j}$ and $l_{k,n}$ are randomly chosen and the generalized gravities $g_{ij}$ and $g_{kn}$ are computed. In the strategy $S_{\rm max}$, the link with larger generalized gravity is added. In the strategy $S_{\rm min}$, the link with smaller generalized gravity is added, which include percolation on the Erd\H os-Rényi random graph and the Achlioptas process of explosive percolation as the limiting cases, $d \to \infty$ and $d \to 0$, respectively. Adjustable strategies facilitate or inhibit the network percolation in a generic view. We calculate percolation thresholds $T_c$ and critical exponents $β$ by numerical simulations. We also obtain various finite-size scaling functions for the node fractions in percolating clusters or arrival of saturation length with different intervening strategies.

physics.soc-ph↗

arXiv2018

Generalized gravity model for human migration

The gravity model (GM) analogous to Newton's law of universal gravitation has successfully described the flow between different spatial regions, such as human migration, traffic flows, international economic trades, etc. This simple but powerful approach relies only on the 'mass' factor represented by the scale of the regions and the 'geometrical' factor represented by the geographical distance. However, when the population has a subpopulation structure distinguished by different attributes, the estimation of the flow solely from the coarse-grained geographical factors in the GM causes the loss of differential geographical information for each attribute. To exploit the full information contained in the geographical information of subpopulation structure, we generalize the GM for population flow by explicitly harnessing the subpopulation properties characterized by both attributes and geography. As a concrete example, we examine the marriage patterns between the bride and the groom clans of Korea in the past. By exploiting more refined geographical and clan information, our generalized GM properly describes the real data, a part of which could not be explained by the conventional GM. Therefore, we would like to emphasize the necessity of using our generalized version of the GM, when the information on such nongeographical subpopulation structures is available.

physics.soc-ph↗

arXiv2017

Benford's Law and First Letter of Word

A universal First-Letter Law (FLL) is derived and described. It predicts the percentages of first letters for words in novels. The FLL is akin to Benford's law (BL) of first digits, which predicts the percentages of first digits in a data collection of numbers. Both are universal in the sense that FLL only depends on the numbers of letters in the alphabet, whereas BL only depends on the number of digits in the base of the number system. The existence of these types of universal laws appears counter-intuitive. Nonetheless both describe data very well. Relations to some earlier works are given. FLL predicts that an English author on the average starts about 16 out of 100 words with the English letter `t'. This is corroborated by data, yet an author can freely write anything. Fuller implications and the applicability of FLL remain for the future.

cs.CL↗