Humans can learn a language through physical interaction with their environment and
semiotic communication with other people. It is very important to obtain a computational …

Humans use signs, eg, sentences in a spoken language, for communication and thought.
Hence, symbol systems like language are crucial for our communication with other agents …

This position paper proposes that the study of embodied cognitive agents, such as
humanoid robots, can advance our understanding of the cognitive development of complex …

We address the problem of bootstrapping language acquisition for an artificial system
similarly to what is observed in experiments with human infants. Our method works by …

This field of research examines how embodied and situated agents, such as robots, evolve
language and thus communicate with each other. This book is a comprehensive survey of …

Constructive studies on symbol emergence systems seek to investigate computational
models that can better explain human language evolution, the creation of symbol systems …

Learning is unsupervised and results from the self-organization of the robot's connectionist
architecture. Results show that the imitative behavior greatly improves the efficiency and …

In this paper we propose a latent Dirichlet allocation (LDA)-based framework for multimodal
categorization and words grounding by robots. The robot uses its physical embodiment to …

This chapter addresses embodied social interaction in life-like agents. Embodiment is
discussed from both artificial intelligence and psychology viewpoints. Different degrees of …

Co-verbal gestures are an important part of human communication, improving its efficiency
and efficacy for information conveyance. One possible means by which such multi-modal …