This paper presents a robust methodology for grounding vocabulary in robots. A social language grounding experiment is designed, where, a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. Any system for grounding vocabulary has to incorporate the properties of gradual evolution and lifelong learning. The learning model of the robot is adopted from an ongoing work on developing systems that conform to these properties. Significant modifications have been introduced to the adopted model, especially to handle words with multiple meanings. A novel classification strategy has been developed for improving the performance of each classifier for each learned category. A set of six new nearest-neighbor based classifiers have also been integrated into the agent architecture. A series of experiments were conducted to test the performance of the new model on vocabulary acquisition. The robot was shown to be robust at acquiring vocabulary and has the potential to learn a far greater number of words (with either single or multiple meanings).

We present a computational model of language learning via a sequence of interactions between a teacher and a learner. The utterances of the teacher and learner refer to shared situations, and the learner uses cross-situational correspondences to learn to comprehend the teacher’s utterances and produce appropriate utterances of its own. We show that in this model the teacher and learner come to be able to understand each other’s meanings. Moreover, the teacher is able to produce meaning-preserving corrections of the learner’s utterances, and the learner is able to detect them. We test our model with limited sublanguages of several natural languages in a common domain of situations. The results show that learning to a high level of performance occurs after a reasonable number of interactions. Moreover, even if the learner does not treat corrections specially, in several cases a high level of performance is achieved significantly sooner by a learner interacting with a correcting teacher than by a learner interacting with a non-correcting teacher. Demonstrating the benefit of semantics to the learner, we compare the number of interactions to reach a high level of performance in our system with the number of similarly generated utterances (with no semantics) required by the ALERGIA algorithm to achieve the same level of performance. We also define and analyze a simplified model of a probabilistic process of collecting corrections to help understand the possibilities and limitations of corrections in our setting. 1

with single-class support vector data

Abstract Naming is a powerful cognitive tool that facilitates categorization by forming an association between words and their referents. There is evidence in child development literature that strong links exist between early word-learning and conceptual development. A growing view is also emerging that language is a cultural product created and acquired through social interactions. Inspired by these studies, this paper presents a novel learning architecture for category formation and vocabulary acquisition in robots through active interaction with humans. This architecture is open-ended and is capable of acquiring new categories and category names incrementally. The process can be compared to language grounding in children at single-word stage. The robot is embodied with visual and auditory sensors for world perception. A human instructor uses speech to teach the robot the names of the objects present in a visually shared environment. The robot uses its perceptual input to ground these spoken words and dynamically form/organize category descriptions in order to achieve better categorization. To evaluate the learning system at word learning and category formation tasks, two experiments were conducted using a simple language game involving naming and corrective feedback actions from the human user. The obtained results are presented and discussed in detail.

Aiding categorization by grounding spoken words- an infant inspired approach to concept formation and language acquisition