Robotic vocabulary building using extension inference and implicit contrast Original Research Article

TWIG (“Transportable Word Intension Generator”) is a system that allows a robot to learn compositional meanings for new words that are grounded in its sensory capabilities. The system is novel in its use of logical semantics to infer which entities in the environment are the referents (extensions) of unfamiliar words; its ability to learn the meanings of deictic (“I,” “this”) pronouns in a real sensory environment; its use of decision trees to implicitly contrast new word definitions with existing ones, thereby creating more complex definitions than if each word were treated as a separate learning problem; and its ability to use words learned in an unsupervised manner in complete grammatical sentences for production, comprehension, or referent inference. In an experiment with a physically embodied robot, TWIG learns grounded meanings for the words “I” and “you,” learns that “this” and “that” refer to objects of varying proximity, that “he” is someone talked about in the third person, and that “above” and “below” refer to height differences between objects. Follow-up experiments demonstrate the systemʹs ability to learn different conjugations of “to be”; show that removing either the extension inference or implicit contrast components of the system results in worse definitions; and demonstrate how decision trees can be used to model shifts in meaning based on context in the case of color words.