FEELIX GROWING

Boucenna, S., Gaussier, P., Andry, P. (2008), "What should be taught first: the emotional expression or the face?" in 8th International conference on Epigenetic Robotics, EPIROB 2008, Brighton, UK, in press

Abstract
We are interested in knowing how a robot head can learn to recognize facial expressions without supervision. Our starting point is a mathematical model showing that a sensory-motor architecture is able to express its emotions succeedes to recognize on-line the facial expression of a caregiver if this latter naturally tends to imitate or to resonate with the system. Interestingly, our works also show that, learning autonomously to recognize a face/non face is more complex than to recognize a facial expression. We propose an architecture using the interaction rhythm to allow first a robust learning of the facial expression without a face tracking and next to perform the learning of the face/non face recognition. Finally we emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more and more complex tasks.

Calinon, S. and Billard, A. (2008), "A framework integrating statistical and social cues to teach a humanoid robot new skills" in Proceedings of the ICRA 2008 Workshop: Social Interaction with Intelligent Indoor Robots (SI3R), Pasadena, CA, USA. May 2008.

Abstract
Bringing robots as collaborative partners into homes presents various challenges to human-robot interaction. Robots will need to interact with untrained users in environments that are originally designed for humans. Compared to their industrial homologous form, humanoid robots can not be preprogrammed with an initial set of behaviours. They should adapt their skills to a huge range of possible tasks without needing to change the environments and tools to fit their needs. The rise of these humanoids implies an inherent social dimension to this technology, where the end-users should be able to teach new skills to these robots in an intuitive manner, relying only on their experience in teaching new skills to other human partners. Our research aims at designing a generic Robot Programming by Demonstration (RPD) framework based on a probabilistic representation of the task constraints, which allows to integrate information from cross-situational statistics and from various social cues such as joint attention or vocal intonation. This paper presents our ongoing research towards bringing user-friendly human-robot teaching systems that would speed up the skill transfer process.

Discrete models:
In discrete models, emotions are the result of a selective adaptation that ensure survival (Darwin 1872). This survival advantage could be illustrated by the following relation: danger → fear → escape → survival. The result of this selection is a small set of basic, innate and universal emotions. For instance, Ekman (1970) proposed 6 basic emotions identified on the basis of facial expressions: anger, disgust, fear, joy, sadness and surprise. Different models have been proposed which include more or less basic emotions, usually from 2 to 10 (Tomkins 1962, Ekman 1970, Izard 1971, Plutchik 1980,...). These emotions are called primary emotions in opposition to secondary emotions which result from a combination of primary ones (e.g. contempt = anger + disgust). From a developmental point of view, basic emotions may be the first emotions infants could experience (Watson 1930).
Dimensional models:
In dimensional models, emotions are defined as a position in a continuous multidimensional space where each dimension stands for a fundamental property common to all emotions. That kind of model was already used by Wilhelm Wundt (Wundt 1905). Over the years, a large number of dimensions has been proposed (Schlosberg 1954, Osgood & al. 1957, Davitz 1969, Lang & al. 1998, Panksepp 1998, Breazeal 2003,...). Two of the most accepted dimensions were described by Russel (Russel 1980): valence (positive versus negative affect) and arousal (low versus high level of activation). This variety of dimensions could be seen as the different expressions of a very small set of basic concepts. Most of these models include two dimensions.

Gaussier, P., Boucenna, S., Nadel, J. (2007) 'Emotional interactions as a way to structure learning'. In: Proceedings of the Seventh International Conference on Epigenetic Robotics (EPIROB 2007), Lund University Cognitive Studies, 135. p193--194.
No Abstract

Hiolle, A., Cañamero, L. & Blanchard, A. (2007). "Learning to interact with the caretaker: a developemental approach", In Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interactions, 2007, 425–-436.
Abstract:
To build autonomous robots able to live and interact with humans in a real-world dynamic and uncertain environment, the design of architectures permitting robots to develop attachment bonds to humans and use them to build their own model of the world is a promising avenue, not only to improve human-robot interaction and adaptation to the environment, but also as a way to develop further cognitive and emotional capabilities. In this paper we present a neural architecture to enable a robot to develop an attachment bond with a person or an object, and to discover the correct sensorimotor associations to maintain a desired affective state of well-being using a minimum amount of prior knowledge about the possible interactions with this object.