Incremental statistical learning for integrating motion primitives and language in humanoid robots

Abstract

We describe a novel approach that allows humanoid robots to incrementally integrate motion primitives and language expressions, when there are underlying natural language and motion language modules. The natural language module represents sentence structure using word bigrams. The motion language module extracts the relations between motion primitives and the relevant words. Both the natural language module and the motion language module are expressed as probabilistic models and, therefore, they can be integrated so that the robots can both interpret observed motion in the form of sentences and generate the motion corresponding to a sentence command. Incremental learning is needed for a robot that develops these linguistic skills autonomously . The algorithm is derived from optimization of the natural language and motion language modules under constraints on their probabilistic variables such that the association between motion primitive and sentence in incrementally added training pairs is strengthened. A test based on interpreting observed motion in the forms of sentence demonstrates the validity of the incremental statistical learning algorithm.