Research Progress

• 05/22 -2026-
  New Progress in LLM + Robot Planning Significantly Boosts Execution Reliability in Intelligent Manufacturing 　　

  In the field of intelligent manufacturing, robot task deployment based on symbolic planning has long been constrained by fragile and error-prone domain models.The research team from the Industrial Control Network and System Department at the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, has proposed a trajectory-guided domain repair framework that achieves precise alignment between symbolic planning models and real-world physical scenarios.
• 05/22 -2026-
  SIA Has Series of Research Papers Accepted by IEEE TIP

  Recently, a series of research outcomes from the Machine Intelligence Research Group at the Robotics Laboratory, the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, have been formally published in IEEE Transactions on Image Processing (TIP), a leading journal in computer vision. The published works address continual video instance segmentation, medical CT image reconstruction, and unsupervised domain adaptive object detection.
• 04/17 -2026-
  Novel BANet Boosts Brain-Computer Interface Signal Decoding Accuracy Dramatically

  The research team led by Professor ZHAO Xingang from the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed a novel EEG decoding network named BANet, based on bridge structures and attention mechanisms. The network consists of three core modules: a convolutional ECA (Efficient Channel Attention) module, a Bridge block, and an Inception-based Temporal Convolutional Network (TCN) module. Among these, the innovatively designed "Bridge block" can extract temporal features of EEG signals from both local and global perspectives, effectively solving the problem that traditional convolutional neural networks focus only on local features while Transformer structures lack sufficient local feature extraction.
• 04/03 -2026-
  AI Model Enhances Precision in Cast Blade Manufacturing

  Recently, a research team from the Manufacturing Equipment and Intelligent Robotics Department at the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed a material removal depth prediction model, O-TabPFN, for robotic abrasive belt grinding processes. This model allows a robot to automatically adjust grinding process parameters based on the distribution of machining allowance across different areas of the blade, enabling precise point-by-point material removal and significantly improving machining accuracy and surface consistency.
• 03/20 -2026-
  New Method Equips Wireless Networked Control Systems with a "Smart Brain"

  Addressing the challenges posed by limited communication resources and highly dynamic environmental conditions in industrial scenarios, the research team led by Professor LIANG Wei from the Industrial Control Network and System Department, the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, has proposed a Joint Estimation-Control-Scheduling (JECS) method based on deep reinforcement learning.
• 03/20 -2026-
  Researchers Propose MMGT for High-Precision Co-Speech Gesture Video Generation from Audio and Image

  Recently, a research team from the Intelligent Detection and Equipment Department at the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed an innovative method for high-quality co-speech gesture video generation. It opens new opportunities for AI-driven content generation in the metaverse and multimedia applications.
• 03/19 -2026-
  Researchers Propose Intelligent Algorithm to Open New Pathways for High-Precision Control of Wireless Cloud Robotic Systems

  Recently, a research team from Industrial Control Network and System Department, the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed an innovative solution. By integrating Reconfigurable Intelligent Surface (RIS) with an advanced algorithm termed "multi-agent transfer reinforcement learning," they successfully achieved co-optimization of robotic control and wireless communication, offering new possibilities for overcoming this bottleneck. The relevant research findings have been published in the leading journal in the field, the IEEE/CAA Journal of Automatica Sinica.
• 03/17 -2026-
  Researchers Developed a CAD-Mesher Module for Achieving Accurate Static Map Construction

  Recently, a research team from Robotics Laboratory, the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed an innovative solution named CAD-Mesher. This module not only effectively filters out interference from dynamic objects to construct high-precision static mesh maps but can also be conveniently integrated into existing LiDAR systems like a "plug-in," providing new technical support for autonomous navigation of robots in complex dynamic environments.
• 03/09 -2026-
  SIA Has Series of Research Papers Accepted by CVPR 2026

  Recently, a series of researchpapers from the Machine Intelligence Research Group at the Robotics Laboratory of the Shenyang Institute of Automation(SIA), Chinese Academy of Sciences(CAS), have been officially accepted by CVPR 2026, a premier international academic conference in the field of computer vision.
• 03/05 -2026-
  SIA Researchers Make Progress in Human-Robot Collaborative Pre-Welding Positioning for Aero-Engine Pipelines

  Recently, a research team from the Manufacturing Equipmeng and Intelligent Robot Department at the Shenyang Institute of Automation(SIA) , Chinese Academy of Sciences(CAS) , has developed a pre-welding positioning system for aero-engine pipelines. This system enables flexible fixture setup for pipelines of varying dimensions.