Fully Neuromorphic Visual and Control Autonomous Aerial Vehicle Background and Research Motivation Over the past decade, deep artificial neural networks (ANNs) have made significant advancements in the field of artificial intelligence, particularly in visual processing. However, these advanced visual processing technologies, despite achieving high ...
Deep Reinforcement Learning Empowers Agile Soccer Skills for Bipedal Robots Background Introduction One of the long-term goals of artificial intelligence (AI) research is to enable agents to exhibit agility, flexibility, and understanding in the physical world. However, animals and humans not only smoothly complete complex physical actions but also...
Autonomous Navigation and Walking Wheel-Leg Robot Background Introduction The acceleration of urbanization has posed significant challenges for supply chain logistics, especially for last-mile delivery. As traffic pressure increases and the demand for faster delivery services rises, particularly with complex routes indoors and on city streets, trad...
Stereoscopic Artificial Compound Eyes for Spatiotemporal Perception in Three-Dimensional Space This research article was published on May 15, 2024, in the journal Science Robotics, titled “Stereoscopic Artificial Compound Eyes for Spatiotemporal Perception in Three-Dimensional Space.” The first author is Byungjoon Bae, and the corresponding author ...
Real-World Humanoid Robot Walking Based on Reinforcement Learning Background Introduction Humanoid robots have enormous potential for autonomous operation in diverse environments, not only alleviating labor shortages in factories but also assisting elderly people at home and exploring new planets. Although classical controllers show excellent effec...
Ultra-wide Field-of-view Pinhole Compound Eye Based on Hemispherical Nanowire Array for Robotic Vision In the rapid development of contemporary artificial intelligence and robotics technology, the visual system, as a crucial component, has attracted widespread attention and in-depth research. According to a research paper published by Zhou et al. o...
Multi-template Meta-information Regularized Network for Alzheimer’s Disease Diagnosis: A Study Based on Structural MRI Research Background Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder, and its diagnosis and early detection is a significant challenge in the medical field. Structural MRI (sMRI) is widely used in computer-aided...
Application of Implicit Regularization Learning Network Based on Noise Generation and Imaging Mechanisms in Low-Dose CT Reconstruction Low-Dose Computed Tomography (LDCT) has become an important tool in modern medical imaging, aiming to reduce radiation risks while maintaining image quality. However, reducing the X-ray dose often leads to data corr...
Unsupervised Fusion of Unaligned PAT and MRI Images Using Mutually Enhancing Cross-Modality Image Generation and Registration Methods Background and Research Objectives In recent years, photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) have been widely used in preclinical research as cutting-edge biomedical imaging techniques. PAT...
Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation Background Introduction Medical image segmentation plays a crucial role in clinical diagnosis as it helps doctors identify and analyze diseases. However, this task typically faces challenges such as sensitive data, privacy issues, and expensive annotation costs. W...