A characteristic shared by most approaches to natural language understanding and generation is the use of symbolic representations of word and sentence meanings. Frames and semantic nets are examples of symbolic representations. Symbolic methods are inappropriate for applications which require natural language semantics to be linked to perception, as is the case in tasks such as scene description or human-robot interaction. This paper presents two implemented systems, one that learns to generate, and one that learns to understand visually-grounded spoken language. These implementations are part of our ongoing effort to develop a comprehensive model of perceptually-grounded semantics.