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In this paper we study the learning of affordances through self-experimentation. We study the learning of local visual descriptors that anticipate the success of a given action executed upon an object. Consider, for instance, the case of grasping. Although graspable is a property of the whole object, the grasp action will only succeed if applied in the right part of the object. We propose an algorithm...
In this paper, we present preliminary results demonstrating how a robot can learn environmental affordances in terms of the features that predict successful control and interaction. We extend previous work in which we proposed a learning framework that allows a robot to develop a series of hierarchical, closed-loop manipulation behaviors. Here, we examine a complementary process where the robot builds...
English learners typically have vocabularies that are dominated by nouns, whereas naturalistic observations, parental checklists and word mapping experiments reveal that verbs, primarily action words, are acquired early and in large quantities by learners of Mandarin Chinese. However, little research has examined whether English and Mandarin learners' early comprehension and production of nouns and...
Much research in developmental psychology and cognitive development presumes a universal developmental trend that is independent of culture. One such trend, from object to relational knowledge, is seen over and over. However, most of this research is based on the study of children and individuals from Western cultures. This paper considers the possibility that this developmental trend might differ...
In this paper we present a framework for the learning and use of sensorimotor schemata. Therefore, we introduce the concept of a schema as a compact representation of an attractor dynamic and discuss how schemata, if embedded into the proposed architecture, can be used to produce, simulate, or recognize goal-directed behaviors. We further present a first implementation of the framework which incorporates...
Bayesian inference techniques have been used to understand the performance of human subjects on a large number of sensory tasks. Particularly, it has been shown that humans integrate sensory inputs from multiple cues in an optimal way in many conditions. Recently it has also been proposed that causal inference can well describe the way humans select the most plausible model for a given input. It is...
Recent years have seen a growing number of experiments in which decision making is studied in the context of visuo-motor decision tasks. In these visuo-motor experiments, the outcome of a hand or eye movement is associated with explicit gains and losses (see, e.g. [1]). The results obtained from these studies indicate that human participants typically select surprisingly efficient strategies that...
This paper presents a discussion and simulation results which support the case for interaction during the acquisition of conceptual knowledge. Taking a developmental perspective, we first review a number of relevant insights on word-meaning acquisition in young children and specifically focus on concept learning supported by linguistic input. We present a computational model implementing a number...
What can a robot learn about the structure of its own body when he does not already know the semantics, the type and the position of its sensors and motors? Previous work has shown that an information theoretic approach, based on pairwise Crutchfield's information distance on sensorimotor channels, could allow to measure the informational topology of the set of sensors, i.e. reconstruct approximately...
IAC was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without pre-programming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared...
A great mystery is how the brain abstracts during the process of development. It is also unclear how motor actions alter cortical representation. The architecture theory introduced here indicates that for each cortical area, the bottom-up space and top-down space are two sources of its representation-bridge representation which embeds manifolds of both spaces into a single space. A bridge representation...
Learning from sensory patterns associated with different kinds of sensors is paramount for biological systems, as it permits them to cope with complex environments where events rarely appear twice in the same way. In this paper we want to investigate how perceptual categories formed in one modality can be transferred to another modality in biological and artificial systems. We first present a study...
In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive...
This research addresses the challenge of developing an action learning model employing bottom-up visual attention. Although bottom-up attention enables robots to autonomously explore the environment, learn to recognize objects, and interact with humans, the instability of their attention as well as the poor quality of the information detected at the attentional location has hindered the robots from...
Emotion modeling evoked by natural scenes is challenging issue. In this paper, we propose a novel scheme for analyzing the emotion reflected by a natural scene, considering the human emotional status. Based on the concept of original GIST, we developed the fuzzy-GIST to build the emotional feature space. According to the relationship between emotional factors and the characters of image, L*C*H* color...
This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate...
In various engineering fields, separation between signal and noise is one of the important issues for the robustness of systems that are working in the real environment, and the noise reduction has been discussed when designing a robust system. On the other hand, some researches reported that adding a noise contributes to having multiple internal states and enhancing a transition between the states...
Cognitive developmental robotics (CDR) (Asada et al., 2001) has been tackling the issue of how human cognitive functions develop by means of a synthetic approach that developmentally constructs cognitive functions. ldquoPhysical embodimentrdquo is the core idea of CDR, and is revisited to make its role clearer, that is, to enable information structuring through interactions with the environment, including...
Due to the pressure of evolution, the brains of organisms need to self-organize at different scales during different developmental stages. In early stages, the brain must organize globally (e.g., large cortical areas) to form ldquosmoothrdquo topographic representation that is critical for superior generalization with its limited connections. At later stages, the brain must fine tune its microstructures...
This paper proposes an interactive approach to object categorization that is consistent with the principle that a robot's object representations should be grounded in its sensorimotor experience. The proposed approach allows a robot to: 1) form object categories based on the movement patterns observed during its interaction with objects, and 2) learn a perceptual model to generalize object category...
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