Research
Themes, models, and applications
Research directions spanning semantic modeling, cognitive systems, robotic interaction, and industrial intelligence.
Research focus
Focused research challenges
- Hypergraph-based semantic models
- Hypergraph-based cognitive systems
- Specification-based monitoring of test environments
- Industrial applications based on hypergraph models
- Tensor-based models of cognitive systems
- Sparse representation in neural networks
- Behavior definition based on hypergraph models
- Structural entropy feedback in neural networks
- Structured prompting based on hypergraph definitions
- Simulation definition and analysis based on hypergraph models and LLMs
Research background
Background research challenges
- Autonomous vehicle trajectory following
- Precise geolocation-based localization
- Etho-robotics and interaction modeling
- Decision processes of autonomous systems
- Agricultural applications
- Model-based environment generation
- Specification-based monitoring of test environments
- Simulator-based V&V of autonomous systems
Behavior analysis
Formal behavior descriptions, adaptive observation pipelines, and semantic representations for complex autonomous and human-centered systems.
- Behavior observation pipelines for structured analysis
- Semantic abstractions for decision and interaction patterns
- Interpretability-oriented modeling of complex systems
HyMeKo
Hypergraph-based semantic modeling for representing knowledge, behavior, and cognitive structure across robotics and intelligent systems.
- Knowledge representation grounded in hypergraph structures
- Cognitive modeling through semantic relations and transformations
- Bridging symbolic structure with autonomous system behavior
Etho-robotics
Interaction modeling inspired by ethology, with a focus on natural behavior, cooperative autonomy, and interpretable decision-making.
- Ethology-inspired interaction modeling for embodied agents
- Naturalistic behavior design in collaborative contexts
- Readable decision processes for human-robot interaction
Industrial-robotics
Applied robotic solutions for industrial environments, combining safe automation, monitoring, and model-driven system design.
- Monitoring and verification in production environments
- Model-based design for robust automation workflows
- Industrial deployment with traceability and safety in mind
Industry 4.0
Connected manufacturing systems, digital twins, smart monitoring, and AI-assisted operational intelligence for modern production contexts.
- Digital twin and smart monitoring strategies
- Connected industrial systems with AI-assisted insight
- Cross-layer data interpretation for adaptive operations