Future robots are expected to communicate with humans using natural language. The nave human user will expect a robot to easily understand what he/she is meaning by instructions concerning robot's tasks. This implies that the robot will need to have a means of grounding, in its own sensors, the natural language terms and constructions used by the human user. This paper presents an approach to solve this problem that is based on the integration of a "learning server" in the software architecture of the robot. Such server should be capable of on-line, incremental learning from examples

The inherent value system of a developmental agent enables autonomous mental development to take place right after the agent’s “birth. ” Biologically, it is not clear what basic components constitute a value system. In the computational model introduced here, we propose that inherent value systems should have at least three basic components: punishment, reward and novelty with decreasing weights from the first component to the last. Punishments and rewards are temporally sparse but novelty is temporally dense. We present a biologically inspired computational architecture that guides development of sensorimotor skills through real-time interactions with the environments, driven by an inborn value system. The inherent value system has been successfully tested on an artificial agent in a simulation environment and a robot in the real world.

Abstract — We describe the design of an anthropomorphic robot head intended as a research platform for studying autonomously learning active vision systems. The robot head closely mimics the major degrees of freedom of the human neck/eye apparatus and allows a number of facial expressions. We show that our robot head can shift its direction of gaze at speeds which come close to that of human saccades. Since our design only makes use of low cost consumer grade components, it paves the way for widespread use of anthropomorphic robot heads in science, education, health-care, and entertainment. I.

Abstract — We present a developmental perspective of robot learning that uses affordances as the link between sensory-motor coordination and imitation. The key concept is a general model for affordances able to learn the statistical relations between actions, object properties and the effects of actions on objects. Based on the learned affordances, it is possible to perform simple imitation games providing both task interpretation and planning capabilities. To evaluate the approach, we provide results of affordance learning with a real robot and simple imitation games with people. Index Terms — robotic development, affordances, imitation I.

Abstract—This paper elaborates on mechanisms for establishing visual joint attention for the design of robotic agents that learn through natural interfaces, following a developmental trajectory not unlike infants. We describe first the evolution of cognitive skills in infants and then the adaptation of cognitive development patterns in robotic design. A comprehensive outlook for cognitively inspired robotic design schemes pertaining to joint attention is presented for the last decade, with particular emphasis on practical implementation issues. A novel cognitively inspired joint attention fixation mechanism is defined for robotic agents. I.

Abstract — It can be very difficult to create software systems which capture the knowledge of an expert. It is an expensive and laborious process that often results in a suboptimal solution. This article proposes an approach which is different from ’manual ’ knowledge construction. The described system is relevant and usable for the end user, from the beginning of its development. It is continuously being trained by the experts while they are using it. This paper shows an example and explains how the computer learns from the experts. In order to provide a general mechanism, the principle of learning to learn is proposed and applied to the problem of handwriting recognition. The study pays attention to the development of the hypotheses that the developing system uses as it adapts to feedback it receives from its own actions on itself, on objects, on the users of the system and the reflection on this feedback. The ultimate goal is the creation of a system that learns to ’google ’ through handwritten documents, starting from scratch with a pile of raw images. I.

Abstract — This paper proposes a novel model which enables a humanoid robot infant to discover other individual (e.g. human parent). In this work, the authors define “other individual” as an actor which can be predicted by a self-model. For modeling the developmental process of discovering ability, the following three approaches are employed. (i) Projection of a selfmodel for predicting other individual’s actions. (ii) Mediation by a physical object between self and other individual. (iii) Introduction of infant imitation by parent. For creating the self-model of a robot, we apply Recurrent Neural Network with Parametric Bias (RNNPB) model which can learn the robot’s body dynamics. For the other-model of a human, conventional hierarchical neural networks are attached to the RNNPB model

How to represent own body is one of the most interesting issues in cognitive developmental robotics which aims to understand the cognitive developmental processes that an intelligent robot would require and how to realize them in a physical entity. This paper presents a cognitive model how the robot acquires its own body representation, that is body scheme for the body surface. The internal observer assumption makes it difficult for a robot to associate the sensory infor- mation from different modalities because of the lacking of references between them that are usually given by the designer in the prenatal stage of the robot. Our model is based on cross-modal map learning among joint, vision, and tactile sensor spaces by associating different pairs of sensor values when they are activated simultaneously. We show a preliminary experiment, and then discuss how our model can explain the reported phenomenon on body scheme and future issues.

Abstract — Coding can contribute to robot design by suggesting behavioural benchmarks. These, however, depend on the level of analysis. In illustration, semi-formalised rules are used to investigate child-robot encounters. By using behaviour-level codes, we extract information about how children use the robot. This leads to findings about longditudinal changes in how children evaluate its behaviours. Children, we find, use the robot as a social mediator – to prompt synchronized social events. By focusing on a behavioural level, coding can benefit designers of robots, software and sensors. C I.

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