Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions
This work addresses the challenge of commonsense knowledge representation for cognitive robotics, though it appears incremental in building on existing ontological and logic programming methods.
The paper tackles the problem of grounding human-object interactions by proposing a deep semantic ontology for space and motion, and demonstrates its formalization and application in reasoning with data from everyday activities and RGBD sensors.
We propose a deep semantic characterization of space and motion categorically from the viewpoint of grounding embodied human-object interactions. Our key focus is on an ontological model that would be adept to formalisation from the viewpoint of commonsense knowledge representation, relational learning, and qualitative reasoning about space and motion in cognitive robotics settings. We demonstrate key aspects of the space & motion ontology and its formalization as a representational framework in the backdrop of select examples from a dataset of everyday activities. Furthermore, focussing on human-object interaction data obtained from RGBD sensors, we also illustrate how declarative (spatio-temporal) reasoning in the (constraint) logic programming family may be performed with the developed deep semantic abstractions.