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Manipulation tasks in daily life, such as pouring water, unfold intentionallyunder specialized manipulation contexts. Being able to process contextualknowledge in these Activities of Daily Living (ADLs) over time can help usunderstand manipulation intentions, which are essential for an intelligentrobot to transition smoothly between various manipulation actions. In thispaper, to model the intended concepts of manipulation, we present a visiondataset under a strictly constrained knowledge domain for both robot and humanmanipulations, where manipulation concepts and relations are stored by anontology system in a taxonomic manner. Furthermore, we propose a scheme togenerate a combination of visual attentions and an evolving knowledge graphfilled with commonsense knowledge. Our scheme works with real-world camerastreams and fuses an attention-based Vision-Language model with the ontologysystem. The experimental results demonstrate that the proposed scheme cansuccessfully represent the evolution of an intended object manipulationprocedure for both robots and humans. The proposed scheme allows the robot tomimic human-like intentional behaviors by watching real-time videos. We aim todevelop this scheme further for real-world robot intelligence in Human-RobotInteraction.

Understanding Contexts Inside Robot and Human Manipulation Tasks through a Vision-Language Model and Ontology System in a Video Stream