Search

International audience; Robots are progressively moving into spaces that have been primarily shaped by human agency; they collaborate with human users in different tasks that require them to understand human language so as to behave appropriately in space. To this end, a stubborn challenge that we address in this paper is inferring the syntactic structure of language, which embraces grounding parts of speech (e.g., nouns, verbs, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) in situated human-robot interaction. This could pave the way towards making a robot able to understand the syntactic relationships between words (i.e., understand phrases), and consequently the meaning of human instructions during interaction, which is a future scope of this current study. I. INTRODUCTION Creating interactive social robots able to collaborate with human users in different tasks requires high-level spatial intelligence that could make them able to discover and interact with their surroundings. Developing this spatial intelligence involves grounding language (action verbs, object characteristics (i.e., color and geometry), and spatial prepositions) and the underlying syntactic structure through sensory information so as to make a robot able to understand human instructions in the physical world. Understanding syntactic structure of language has been intensively investigated in the literature of cognitive robotics and computational linguistics. In cognitive robotics, different research studies proposed computational models for grounding nouns, verbs, adjectives, and prepositions encoding spatial relationships between objects [1, 2, 22, 26, 38]. However, they have not investigated grammar understanding at the phrase level, which constitutes a higher level than grounding words through perception. Meanwhile, in computational linguistics, recent studies presented models for inducing combinatory syntactic structure of language [5, 15]; however, they used annotated databases for grammar induction where each word has a corresponding syntactic tag (as a noun, verb, etc.). This last point illustrates the important role that cognitive robotics could play in grammar induction through grounding parts of speech in visual perception so as to allow for learning the latent syntactic structure of phrases in a developmentally plausible manner. In this study, we build on the model of Bisk and Hockenmaier [5] for grammar induction, and propose an extended probabilistic

International audience

Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, none of them considered grounding of synonyms. In this paper, we try to fill this gap by introducing a Bayesian learning model for grounding synonymous object and action names using cross-situational learning. Three different word representations are employed with the probabilistic model and evaluated according to their grounding performance. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed model. The results show that representing words by syntactic and/or semantic information achieves worse grounding results than representing them by unique numbers.

In this paper, we present an unsupervised probabilistic framework to grounding words (e.g., nouns, verbs, adjectives, and prepositions) through visual perception, and we discuss grammar induction in situated human-robot interaction with the objective of making a robot able to understand the underlying syntactic structure of human instructions so as to collaborate with users in space efficiently.

In this paper, we proposed a novel serious game for decentralized autonomous electric power network called i-Rene （inter intelligent renewable energy network）. Renewable energy sources and micro energy-storages have been attracted in years past. We have proposed a concept which not only improves energy independence but also achieves an optimal allocation of renewables by introducing market mechanisms based on artificial learning multi-agent system. Although such decentralized network would play an important role to install further renewable energy, there is no similar system in this real world today. Many people are unfamiliar with such renewable energy network. It means that the many people are not ready to adopt such decentralized autonomous electric power network. This has been an invisible barrier to install the new electric power network. To solve this problem, we propose to use a serious game. We developed a handon game simulating i-Rene based on serious game concepts. We evaluate the performance of this platform through two experiments.