CALL FOR PAPERS

2026 International Conference on Intelligence Science and Cyber-Physical Technologies (ISCPT 2026) is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and practical intelligence science and cyber-physical technologies. The conference will bring together leading researchers, engineers, and practitioners in the domain of interest from around the world.

Call for Papers includes, but is not limited to, the following topics:

Track 1: Robotics and Autonomous Systems * Robot perception and environmental interaction * Multimodal sensing and sensor fusion * Autonomous navigation in unstructured environments * Soft robotics and bio-inspired systems * Human–robot collaboration and safety in shared spaces * Coordination and control of multi-robot systems * Medical, rehabilitation, and assistive robotics * Robotics for extreme environments * Robot learning and embodied AI * Micro/nano robotics and biomedical applications * Robot motion planning and control * AI-enabled robot autonomy * Generative AI for robot simulation and design * Robot manipulation and dexterous grasping * Robot digital twins from a cyber-physical perspective

Track 2: Intelligent Control and Cyber-Physical Systems * Cooperative control of multi-agent systems * Advanced control theory and algorithm design * Intelligent control for Industry 4.0 and smart manufacturing * AI-powered control of energy systems * Autonomous driving and intelligent transportation systems * Networked and distributed control systems * Cyber-physical systems for biomedical applications * Smart buildings and home automation * Precision agriculture and environmental control * Quantum and neuromorphic control algorithms * Multiphysics systems and collaborative control * Industrial IoT and intelligent sensing * Secure communication and cooperative control * Edge intelligence for real-time control * Learning-based control and system identification

Track 3: Artificial Intelligence and Data-Driven Intelligence * Deep learning and foundation models * Advances in computer vision and multimodal understanding * Large language models for autonomous systems * Reinforcement learning and decision intelligence * AI for scientific discovery (AI4Science) * Efficient and embedded AI systems * Generative AI and its applications in robotics * Trustworthy AI: fairness, accountability, and transparency * Neuro-symbolic AI and brain-inspired computing * AI-driven autonomous systems * Multimodal perception and generative models * Federated learning for edge intelligence * Explainable AI (XAI) for autonomous systems * AI safety, robustness, and verification * Artificial intelligence in cyber-physical systems