Xiaodong Chen

YNICL Journal 2025 Journal Article

Alterations of long-range association fibers in patients with anti-N-methyl-D-aspartate receptor encephalitis

• Xiaodong Chen
• Ling Fang
• Yiying Huang
• Yu Huang
• Yi Lu
• Jinhui Wang
• Chunxin Liu
• Huanquan Liao

ICRA Conference 2025 Conference Paper

Deep Learning Based Topography Aware Gas Source Localization with Mobile Robot

• Changhao Tian
• Annan Wang
• Han Fan
• Thomas Wiedemann 0002
• Yifei Luo
• Le Yang 0007
• Weisi Lin
• Achim J. Lilienthal

Gas source localization in complex environments is critical for applications such as environmental monitoring, industrial safety, and disaster response. Traditional methods often struggle with the challenges posed by a lack of environmental topography integration, especially when interactions between wind and obstacles distort gas dispersion patterns. In this paper, we propose a deep learning-based approach, which leverages spatial context and environmental mapping to enhance gas source localization. By integrating Simultaneous Localization and Mapping (SLAM) with a U-Net-based model, our method predicts the likelihood of gas source locations by analyzing gas sensor data, wind flow, and topography of the environment represented by a 2D occupancy map. We demonstrate the efficacy of our approach using a wheeled robot equipped with a photoionization detector, a LIDAR, and an anemometer, in various scenarios with dynamic wind fields and multiple obstacles. The results show that our approach can robustly locate gas sources, even in challenging environments with fluctuating wind directions, outperforming conventional methods by utilizing topography contextual information. This study underscores the importance of topographical context in gas source localization and offers a flexible and robust solution for real-world applications. Data and code are publicly available.

ICLR Conference 2025 Conference Paper

Streamlining Redundant Layers to Compress Large Language Models

• Xiaodong Chen
• Yuxuan Hu
• Jing Zhang 0001
• Yanling Wang
• Cuiping Li 0001
• Hong Chen 0001

This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned. LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowest importance based on target sparsity, and layer replacement, a novel module that trains a lightweight network to replace the pruned layers to mitigate performance loss. Additionally, a new metric called stability is proposed to address the limitations of the widely used accuracy metric in evaluating model compression. Experiments show that LLM-Streamline outperforms both previous and concurrent state-of-the-art pruning methods in terms of both performance and training efficiency. Our code is available at \href{https://github.com/RUCKBReasoning/LLM-Streamline}{this repository}.

ICRA Conference 2024 Conference Paper

Sensorized Soft Skin for Dexterous Robotic Hands

• Jana Egli
• Benedek Forrai
• Thomas Buchner
• Jiangtao Su
• Xiaodong Chen
• Robert K. Katzschmann

Conventional industrial robots often use two-fingered grippers or suction cups to manipulate objects or interact with the world. Because of their simplified design, they are unable to reproduce the dexterity of human hands when manipulating a wide range of objects. While the control of humanoid hands evolved greatly, hardware platforms still lack capabilities, particularly in tactile sensing and providing soft contact surfaces. In this work, we present a method that equips the skeleton of a tendon-driven humanoid hand with a soft and sensorized tactile skin. Multi-material 3D printing allows us to iteratively approach a cast skin design which preserves the robot’s dexterity in terms of range of motion and speed. We demonstrate that a soft skin enables firmer grasps and piezoresistive sensor integration enhances the hand’s tactile sensing capabilities.