Research Progress

• 07/29 -2026-
  Researchers Propose Shank-Angle-Driven Control Method for Exoskeleton to Assist Non‑Steady Locomotion

  Recently, a research team from the Robotics Laboratory at the Shenyang Institute of Automation (SIA) of Chinese Academy of Sciences, proposed an intelligent control method for lower-limb exoskeleton robots driven by the shank angle of the human leg. This approach enables the exoskeleton to continuously perceive the human motion state during non‑steady locomotion, achieving continuous matching and bionic coordination between human and robot motion.
• 07/29 -2026-
  Researchers Propose Frequency-Causal Mixture-of-Experts Network for High‑Precision Spinal CT Segmentation

  Automatic segmentation of spinal CT images is a foundational technique for intelligent orthopedic imaging diagnosis, preoperative surgical planning, and intraoperative navigation for spinal robotics. However, the human spine comprises tightly packed vertebral bodies with significant anatomical variations across different segments and complex local textures. To address this challenge, the research team from the Robotics Laboratory at the Shenyang Institute of Automation(SIA) of Chinese Academy of Sciences, proposed a Mixture-of-Experts network via Frequency‑Causal Reasoning (FC‑MoE), offering a novel technical solution for high‑precision automatic segmentation of complex spinal images.
• 07/27 -2026-
  Haidou-1 and Fendouzhe Complete Joint Hadal Scientific Expedition in the Western Pacific

  Supported by the National Key R&D Program project Enhancement of Key Operational Technologies and Scientific Application Capabilities of Haidou-1, the full-ocean-depth Autonomous and Remotely-operated Vehicle (ARV) Haidou-1, developed under the leadership of the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, has recently completed its sea trial and scientific mission in the high seas of the Philippine Sea, Western Pacific, aboard the research vessel Tansuo-3, and safely returned to port.
• 07/21 -2026-
  New Dependency-Aware Method Enhances Information Freshness and Control in Industrial Systems

  To address this limitation, researchers from the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences,developed a dependency-aware co-optimization method for sensing-transmission-computation-control (STCC) systems. The findings were published in IEEE Transactions on Mobile Computing.
• 07/15 -2026-
  Researchers Propose a Novel Algorithmic Framework for Large Model-Empowered Collaborative Optimization of Perception-Communication-Computation-Control

  ​Industrial Cyber-Physical Systems (ICPS) represent a quintessential deep coupling system integrating Sensing, Communication, Computation, and Control (S3C). They have long faced challenges such as high dimensionality, non-convexity, and partial observability. To address these challenges, the research team from Shenyang Institute of Automation(SIA) of Chinese Academy of Sciences, proposed a novel algorithmic framework: LLM-Enhanced Multi-Agent Transfer Reinforcement Learning (LLMPT-MADRL).
• 07/09 -2026-
  K2MUSE: Multimodal Walking Dataset Bri dges Lab-to-Real-World Gap for Rehabilitation Robots

  The ressearch team from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, in collaboration with researchers from other universities, multiple university teams, has have released an open-source multimodal lower-limb walking dataset named K2MUSE.
• 07/09 -2026-
  New Wearable Microneedle Sensing Platform for Dopamine Detection Developed by SIABicatalytic Nanozyme Patch Enables Painless Dopamine Monitoring

  Recently, a research team from the Robotics Laboratory, Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, has made progress in the field of wearable biosensing. Addressing the industrial bottleneck of achieving minimally invasive, real-time, highly sensitive, and accurate detection of dopamine in human skin interstitial fluid, the team innovatively constructed a wearable microneedle sensing platform integrated with a bicatalytic nanozyme, opening up a new technological pathway for dynamic neurotransmitter monitoring, early screening of neurological diseases, and home-based intelligent health monitoring.
• 07/06 -2026-
  A Statistically Informed Neural Network Enables Interpretable and Accurate Industrial Sensing Under Limited Data

  The realization of intelligent manufacturing in the process industry relies on real-time, accurate perception of key parameters during production. However, industrial sites commonly face challenges such as scarce labeled samples, strong noise interference, and complex nonlinear relationships. To address this issue, the LIBS team from the Industrial Control Network and System Department of the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, has proposed a partial least squares-assisted optimization network, termed PLSaoNET.
• 06/17 -2026-
  PAG-DAF: Power-Aware Graph Adaptation for SPND Fault Detection

  A research team from the Industrial Control Network and System Department at the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, has proposed a Power-Aware Graph Domain Adaptation Framework (PAG-DAF) tailored for variable power conditions.
• 05/25 -2026-
  Novel AI Method Enables Intelligent Reservoir Dynamic Simulation

  Recently, a research team from the Industrial Control Network and System Department of the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences, proposed a novel method called PI-DeepOKAN, which deeply integrates physical laws with artificial intelligence, providing an efficient new tool for reservoir dynamic simulation and intelligent decision-making.