Natalia Lyubova

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This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we(More)
We present a visual system for a humanoid robot that supports an efficient online learning and recognition of various elements of the environment. Taking inspiration from child's perception and following the principles of developmental robotics, our algorithm does not require image databases, predefined objects nor face/skin detectors. The robot explores(More)
Service robots, working in evolving human environments, need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to(More)
In this paper we describe a cognitive architecture for humanoids interacting with objects and caregivers in a developmental robotics scenario. The architecture is foundational to the MACSi project: it is designed to support experiments to make a humanoid robot gradually enlarge its repertoire of known objects and skills combining autonomous learning, social(More)
In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven(More)
We present a developmental approach that allows a humanoid robot to continuously and incrementally learn entities through interaction with a human partner in a first stage before categorizing these entities into objects, humans or robot parts and using this knowledge to improve objects models by manipulation in a second stage. This approach does not require(More)
Future service robots will need the ability to work in unpredictable human environments. These robots should be able to learn autonomously without constant supervision in order to adapt to the environment, different users, and changing circumstances. Exploration of unstructured environments requires continuous detection of new objects and learning about(More)
Objectives: We present a cognitive developmental approach for a humanoid robot exploring its close environment in an interactive scenario, taking inspiration from the way infants learn about objects [1]. The proposed approach allows to detect physical entities in the visual space, to create multi-view appearance models of these entities and to categorize(More)
Clustering techniques have been applied to spectral images mostly to classify materials or objects included in the scenes. We present in this study new data concerning the use of clustering algorithms as a preprocessing step for obtaining spectral data from camera responses. Four different clustering algorithms have been used, and the Kohonen maps seem to(More)
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