LAUSR

Future intelligent robots are expected to be able to adapt continuously to their environment. For this purpose, recognizing new objects and learning new words through interactive learning with humans is fundamental. Such setup results in ambiguous teaching data which humans have been shown to address using cross-situational learning, i.e., by analyzing common factors between multiple learning situations. Moreover, they have been shown to be more efficient when actively choosing the learning samples, e.g., which object they want to learn. Implementing such abilities on robots can be performed by latent-topic learning models such as non-negative matrix factorization or latent Dirichlet allocation. These cross-situational learning methods tackle referential and linguistic ambiguities, and can be associated with active learning strategies. We propose two such methods: 1) the maximum reconstruction error-based selection and 2) confidence base exploration. We present extensive experiments using these two learning algorithms through a systematic analysis on the effects of these active learning strategies in contrast with random choice. In addition, we study the factors underlying the active learning by focusing on the use of sample repetition, one of the learning behaviors that have been shown to be important for humans.