Institution profile

Daegu Gyeongbuk Institute of Science and Technology

Academic institutionasia · kr
Official website
Research library124linked papers
Opportunities0open roles
Selected work

Representative Papers

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

Jan 26, 2025

Automatic breast ultrasound (ABUS) tumor detection, segmentation, and classification are challenged by morphological heterogeneity, low signal-to-noise ratio, and scarcity of annotated 3D data. Method: We introduce the first publicly available, high-quality, multi-center ABUS tumor benchmark dataset and the TDSC-ABUS2023 international challenge platform—enabling the first unified three-task evaluation. Our proposed framework integrates multi-scale 3D CNNs, Transformers, semi-supervised learning, and boundary-aware loss to address ABUS-specific challenges including ill-defined tumor boundaries and low contrast. Contribution/Results: Our method achieves state-of-the-art performance: 82.3% mAP@0.5 for detection, 79.6% Dice for segmentation, and 91.4% accuracy for malignancy classification—significantly outperforming baselines. This work fills critical gaps in publicly accessible ABUS benchmarks and standardized multi-task evaluation, advancing intelligent early diagnosis of breast cancer.

2 citationsRead paper

A Review of Online Diffusion Policy RL Algorithms for Scalable Robotic Control

Jan 05, 2026arXiv.org

This work addresses the challenge in online diffusion policy reinforcement learning (Online DPRL) where training objectives and policy improvement mechanisms are often misaligned, hindering scalable robotic control. We propose the first taxonomy of Online DPRL algorithms based on their policy improvement mechanisms, categorizing them into four classes: Action-Gradient, Q-Weighting, Proximity-Based, and BPTT. Leveraging the NVIDIA Isaac Lab platform, we conduct a unified benchmark across twelve diverse robotic tasks and systematically evaluate these methods along five critical dimensions: task diversity, parallelizability, diffusion-step scalability, cross-embodiment generalization, and environmental robustness. Our analysis reveals fundamental trade-offs between sample efficiency and scalability, identifies key bottlenecks limiting real-world deployment, and provides practical guidance for algorithm selection while highlighting promising directions for future research.

1 citationsRead paper

LAMP: Implicit Language Map for Robot Navigation

Dec 01, 2025IEEE Robotics and Automation Letters

This work addresses the limitations of existing explicit language-embedded maps in large-scale navigation, which suffer from high memory overhead and low spatial resolution, thereby hindering fine-grained path planning. The authors propose LAMP, a novel framework that introduces an implicit neural language field to construct a continuous, language-driven map. LAMP first performs coarse path planning using a sparse graph and then refines the agent’s pose near the target through language-guided gradient optimization. A key innovation lies in the integration of Bayesian von Mises–Fisher (vMF) distributions to model uncertainty in language embeddings, significantly enhancing generalization to unobserved regions. Experiments demonstrate that LAMP substantially outperforms current methods in both NVIDIA Isaac Sim simulations and real-world multi-floor environments, achieving breakthroughs in memory efficiency and fine-grained goal-reaching accuracy.

1 citationsRead paper
Recent publications

Latest Papers

Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter

Aug 31, 2026

Multi-robot informative path planning (IPP) for persistent target monitoring requires robots to reason about spatial uncertainty, temporal evolution, and practical sensing and communication constraints. Recent learning-based multi-robot IPP methods use Gaussian Processes (GPs) for target uncertainty, but often rely on simplified sensing models and centralized belief updates. We propose a grid-based spatio-temporal GP-Kalman filtering framework for learning-based multi-robot IPP. Instead of maintaining one GP per target, we represent anonymous target presence as a single latent field over a discrete workspace grid. The proposed recursive update considers all visible cells inside a camera footprint and supports arbitrary fields of view and range-dependent noise. A GP-consistent temporal process update accounts for moving targets and stale information by inflating uncertainty over time. For decentralized deployment, each robot maintains its own mapper and exchanges compact belief summaries rather than raw measurements. Received beliefs are fused using diagonal covariance intersection to remain conservative under unknown inter-robot correlations. We integrate the mapper with a reinforcement-learning policy for graph-based neighbor selection. Simulation benchmarks show about 20% lower average target uncertainty and improved target visitation compared with learning-based and classical auction/coverage baselines. Real-world two-UAV experiments demonstrate transfer to outdoor multi-robot search over a large field of more than 7000 square meters.

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