  We present a commonsense, qualitative model for the semantic grounding of
embodied visuo-spatial and locomotive interactions. The key contribution is an
integrative methodology combining low-level visual processing with high-level,
human-centred representations of space and motion rooted in artificial
intelligence. We demonstrate practical applicability with examples involving
object interactions, and indoor movement.
