HMM-based concept learning for a mobile robot

We are developing an intelligent robot and attempting to teach it language. While there are many aspects of this research, for the purposes here the most important are the following ideas. Language is primarily based on semantics, not syntax, which is still the focus in speech recognition research these days. To truly learn meaning, a language engine cannot simply be a computer program running on a desktop computer analyzing speech. It must be part of a more general, embodied intelligent system, one capable of using associative learning to form concepts from the perception of experiences in the world, and further capable of manipulating those concepts symbolically. In this paper, we present a general cascade model for learning concepts, and explore the use of hidden Markov models (HMMs) as part of the cascade model. HMMs are capable of automatically learning and extracting the underlying structure of continuous-valued inputs and representing that structure in the states of the model. These states can then be treated as symbolic representations of the inputs. We show how a cascade of HMMs can be embedded in a small mobile robot and used to find correlations among sensory inputs to learn a set of symbolic concepts, which are used for decision making and could eventually be manipulated linguistically.