Online Algorithm for Robots to Learn Object Concepts and Language Model

Abstract

Humans form concept of objects by classifying them into categories, and acquire language by simultaneously interacting with others. Thus, the meaning of a word can be learned by connecting a recognized word to its corresponding concept. We consider this ability important for robots to flexibly develop knowledge of language and concepts. In this paper, we propose an online algorithm for robots to acquire knowledge of natural language and learn object concepts. A robot learns the language model from word sequences, which are obtained by the segmentation of phoneme sequences provided by a user, by using unsupervised word segmentation each time it is provided with a new object. Moreover, the robot acquires object concepts using these word sequences as well as multimodal information obtained by observing objects. The crucial aspect of our model is the interdependence of words and concepts: there is a high probability that the same words will be uttered to describe objects in the same category. By taking this relationship into account, our proposed method enables robots to acquire a more accurate language model and object concepts online. Experimental results verify this.

Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models

“…A further advancement of such cognitive systems allows the robots to find meanings of words by treating a linguistic input as another modality [13][14][15]. Cognitive models have recently become more complex in realizing various cognitive capabilities: grammar acquisition [16], language model learning [17], hierarchical concept acquisition [18,19], spatial concept acquisition [20], motion skill acquisition [21], and task planning [7] (see Fig. 1).…”

Symbol Emergence in Cognitive Developmental Systems: A Survey

“…Creating a robot that can learn language from its own sensorimotor experience alone is one of our challenges, which is an essential element for the understanding of symbol emergence in cognitive systems. Many studies have been exploring the challenge in modeling language acquisition in developmental process using neural networks [91], [124], [152] and probabilistic models [5], [88], [153], [154].…”

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“…The NPYLM is an unsu-pervised morphological analysis method based on a statistical model that enables word segmentation exclusively from phoneme sequences (Mochihashi et al 2009). In addition, Nishihara et al (2017) was able to reduce phoneme recognition errors by applying PFoMDLA to inferences using a particle filter instead of oMLDA. In these studies, online learning was realized as an algorithm in unsupervised machine learning.…”