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Understanding manipulation scenarios allows intelligent robots to plan forappropriate actions to complete a manipulation task successfully. It isessential for intelligent robots to semantically interpret manipulationknowledge by describing entities, relations and attributes in a structuralmanner. In this paper, we propose an implementing framework to generatehigh-level conceptual dynamic knowledge graphs from video clips. A combinationof a Vision-Language model and an ontology system, in correspondence withvisual perception and contextual semantics, is used to represent robotmanipulation knowledge with Entity-Relation-Entity (E-R-E) andEntity-Attribute-Value (E-A-V) tuples. The proposed method is flexible andwell-versed. Using the framework, we present a case study where robot performsmanipulation actions in a kitchen environment, bridging visual perception withcontextual semantics using the generated dynamic knowledge graphs.

Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks