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 sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

Traditionally, in robotics, artificial intelligence, and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest into the notion of embodiment in all disciplines dealing with intelligent behavior, including psychology, philosophy, and linguistics. In this paper, we explore the far-reaching and often surprising implications of this concept. While embodiment has often been used in its trivial meaning, i.e. „intelligence requires a body“, there are deeper and more important consequences, concerned with connecting brain, body, and environment, or more generally with the relation between physical and information (neural, control) processes. It turns out that, for example, robots designed by exploiting embodiment are frequently simpler, more robust and adaptive than those based on the classical control paradigm. Often, morphology and materials can take over some of the functions normally attributed to control, a phenomenon called “morphological computation”. It can be shown that through the embodied interaction with the environment, in particular through sensory-motor coordination, information structure is induced in the sensory data, thus facilitating perception and learning. A number of case studies are presented to illustrate the concept of embodiment. We conclude with some speculations about potential lessons for robotics.

Abstract—Infants learning about their environment are confronted with many stimuli of different modalities. Therefore, a crucial problem is how to discover which stimuli are related, for instance, in learning words. In making these multimodal “bindings”, infants depend on social interaction with a caregiver to guide their attention towards relevant stimuli. The caregiver might, for example, visually highlight an object by shaking it while vocalizing the object’s name. These cues are known to help structuring the continuous stream of stimuli. To detect and exploit them, we propose a model of bottom-up attention by multimodal signal-level synchrony. We focus on the guidance of visual attention from audio-visual synchrony informed by recent adult-infant interaction studies. Consequently, we demonstrate that our model is receptive to parental cues during child-directed tutoring. The findings discussed in this paper are consistent with recent results from developmental psychology but for the first time are obtained employing an objective, computational model. The presence of “multimodal motherese ” is verified directly on the audio-visual signal. Finally, we hypothesize how our computational model facilitates tutoring interaction and discuss its application in interactive learning scenarios, enabling social robots to benefit from adult-like tutoring.

Aristotelian, or non-Platonist, realism holds that mathematics is a science of the real world, just as much as biology or sociology are. Where biology studies living things and sociology studies human social relations, mathematics studies the quantitative or structural aspects of things, such as ratios, or patterns, or complexity,

The current review focuses on how exposure to linguistic input, and count nouns in particular, affect performance on various cognitive tasks, including individuation, categorization and category learning, and inductive inference. We review two theoretical accounts of effects of words. Proponents of one account argue that words have top-down effects on cognitive tasks, and, as such, function as supervisory signals. Proponents of the other account suggest that early in development, words, just like any other perceptual feature, are first and foremost part of the stimulus input and influence cognitive tasks in a bottom-up, non-supervisory fashion. We then review evidence supporting each account. We conclude that, although much research is needed, there is a large body of evidence indicating that words start out like other perceptual features and become supervisory signals in the course of development.