Tuấn Anh Nguyễn

Seunghyeop Nam, Tuan Anh Nguyen, Eunmi Choi et al.

Autonomous robot exploration in large-scale or cluttered environments remains a central challenge in intelligent vehicle applications, where partial or absent prior maps constrain reliable navigation. This paper introduces FH-DRL, a novel framework that integrates a customizable heuristic function for frontier detection with a Twin Delayed DDPG (TD3) agent for continuous, high-speed local navigation. The proposed heuristic relies on an exponential-hyperbolic distance score, which balances immediate proximity against long-range exploration gains, and an occupancy-based stochastic measure, accounting for environmental openness and obstacle densities in real time. By ranking frontiers using these adaptive metrics, FH-DRL targets highly informative yet tractable waypoints, thereby minimizing redundant paths and total exploration time. We thoroughly evaluate FH-DRL across multiple simulated and real-world scenarios, demonstrating clear improvements in travel distance and completion time over frontier-only or purely DRL-based exploration. In structured corridor layouts and maze-like topologies, our architecture consistently outperforms standard methods such as Nearest Frontier, Cognet Frontier Exploration, and Goal Driven Autonomous Exploration. Real-world tests with a Turtlebot3 platform further confirm robust adaptation to previously unseen or cluttered indoor spaces. The results highlight FH-DRL as an efficient and generalizable approach for frontier-based exploration in large or partially known environments, offering a promising direction for various autonomous driving, industrial, and service robotics tasks.

Tuan Nguyen, Hirotada Honda, Takashi Sano et al.

We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of continuous-depth graph neural networks (GNNs) that employs the Kuramoto model to mitigate the over-smoothing phenomenon, in which node features in GNNs become indistinguishable as the number of layers increases. The Kuramoto model captures the synchronization behavior of non-linear coupled oscillators. Under the view of coupled oscillators, we first show the connection between Kuramoto model and basic GNN and then over-smoothing phenomenon in GNNs can be interpreted as phase synchronization in Kuramoto model. The KuramotoGNN replaces this phase synchronization with frequency synchronization to prevent the node features from converging into each other while allowing the system to reach a stable synchronized state. We experimentally verify the advantages of the KuramotoGNN over the baseline GNNs and existing methods in reducing over-smoothing on various graph deep learning benchmark tasks.

Junghyun Park, Tuan Anh Nguyen, Dugki Min

Real world deployments often expose modern object recognition models to domain shifts that precipitate a severe drop in accuracy. Such shifts encompass (i) variations in low level image statistics, (ii) changes in object pose and viewpoint, (iii) partial occlusion, and (iv) visual confusion across adjacent classes. To mitigate this degradation, we introduce the Re-Thinking Vision Language Model (RT-VLM) framework. The foundation of this framework is a unique synthetic dataset generation pipeline that produces images annotated with "4-Clues": precise bounding boxes, class names, detailed object-level captions, and a comprehensive context-level caption for the entire scene. We then perform parameter efficient supervised tuning of Llama 3.2 11B Vision Instruct on this resource. At inference time, a two stage Re-Thinking scheme is executed: the model first emits its own four clues, then re examines these responses as evidence and iteratively corrects them. Across robustness benchmarks that isolate individual domain shifts, RT-VLM consistently surpasses strong baselines. These findings indicate that the integration of structured multimodal evidence with an explicit self critique loop constitutes a promising route toward reliable and transferable visual understanding.

Anh Duc Le, Dung Van Pham, Tuan Anh Nguyen

Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed pre-processing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task.