  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.
