We welcome original research papers and industry case studies in the following areas. Please select the track that best matches the main contribution of your work.
1. Industrial Robotics and Autonomous Systems
Developing robots that perceive, plan, learn, move, and act in industrial environments.
- Industrial manipulators and robotic manipulation
- Robot perception and machine vision
- Motion and task planning
- Autonomous mobile robots (AMRs) and automated guided vehicles (AGVs)
- Robot learning and adaptive behavior
- Multi-robot coordination
- Physical AI, embodied AI, and industrial humanoid robots
- Robotic grasping, navigation, and autonomous inspection
2. Intelligent Automation, Control and Human–Robot Collaboration
Integrating machines, control systems, robots, and people into effective, adaptive, and safe industrial operations.
- Intelligent, adaptive, and robust control
- PLC, SCADA, and industrial control integration
- Mechatronics and automated production systems
- Collaborative robots and human–robot interaction
- Human–robot task allocation
- Safety, ergonomics, and human factors in automation
- Reconfigurable and flexible automation
- Human-in-the-loop control
3. AI for Manufacturing and Industrial Processes
Applying and evaluating AI to solve concrete problems in production quality, maintenance, process performance, and resource use.
- AI-based quality inspection and defect detection
- Predictive maintenance and remaining useful life estimation
- Fault diagnosis and condition monitoring
- Machine learning for process modeling and optimization
- AI-assisted process monitoring and parameter adjustment
- AI-enabled digital twins for manufacturing
- AI for energy efficiency and resource optimization
- Industrial deployment and validation of AI models
4. Industrial Data Analytics and Decision Intelligence
Turning industrial data into understanding, forecasts, and defensible decisions through statistics, operations research, optimization, and simulation.
- Statistical modeling and industrial data analysis
- Time-series analysis and forecasting
- Statistical process control and process capability
- Data-driven optimization and operations research
- Simulation-based decision support
- Prescriptive analytics and multi-criteria decision-making
- Uncertainty, risk, and reliability analytics
- Explainable analytics and interpretable decision models
5. Generative AI and Knowledge Systems for Industry
Building and evaluating generative AI and knowledge systems for engineering information, industrial tasks, and technical decision support.
- Large language models for industrial applications
- Retrieval-augmented generation (RAG) and industrial knowledge retrieval
- Engineering and maintenance copilots
- AI agents and tool-using systems
- Industrial knowledge graphs and semantic systems
- Multimodal generative AI for technical content
- Generative design and engineering content generation
- Reliability, factuality, and evaluation of industrial GenAI
6. Industrial IoT, Edge AI and Cyber-Physical Systems
Connecting physical equipment, sensing, communication, and computation to deliver reliable industrial systems under real-world constraints.
- Industrial sensing and data acquisition
- Sensor fusion and distributed monitoring
- Industrial IoT architectures and connectivity
- Embedded AI and edge inference
- Model compression and resource-efficient AI
- Real-time and low-latency industrial systems
- Edge–cloud integration and distributed intelligence
- Cyber-physical integration, interoperability, and security
Interdisciplinary contributions are welcome. The Technical Program Committee may assign submissions to the most appropriate track for review and presentation.