Factory Control
Factory Control studies AI-native machine control and operations autonomy: how AI can understand control semantics, act through workcell agents, reason inside digital twins, and validate robotic assembly logic before deployment.
Research Directions
Semantic representation for AI-native machine control, including Control Semantic Graphs, Part Travel Plans, device grounding, and the semantic-to-control process for generating and validating industrial logic.
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AI agents that perceive workcell states, coordinate devices, make operation decisions, and adapt execution under changing production conditions.
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Executable digital twin environments where AI agents can simulate candidate actions, evaluate control consequences, and refine operation strategies before deployment.
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Validation of robotic assembly control logic by combining physical constraints, simulation models, and AI-based reasoning in virtual commissioning.
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Integration of expert know-how, demonstrations, and implicit operation rules into robotic learning and autonomous execution.
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