Research

Research projects

Active learning, intrinsic motivation and
self-organization of developmental structures

Together with my colleagues, we study computational mechanisms of active exploration for life-long learning, allowing an embodied learner to develop repertoires of novel skills by collecting and structuring autonomously its own learning data from simple to progressively more complex.

This research is framed within, and allows to bridge, three fields: statistical machine learning, developmental robotics, and the study of human development. More info …

Social learning and human-robot interaction

In order to learn efficiently from interactions with humans who are not engineers, robots do not only need sophisticated learning and perceptual algorithms: interaction design and adaptive interfaces are essential. Indeed, robots eventually learn based on the training data they collect through social human-robot interaction. Thus, the quality of this data is crucial. The vision that explored here is that considerable learning efficiency can be gained by designing and using adequate human-robot interfaces, i.e. interfaces that are both easy to learn by the human, easy to use, and constrain the interaction so that the robot gets seemlessly high-quality training examples from a non-expert user. To achieve this target, it is in particular of high-importance that interfaces and human-robot interaction protocols become themselves adaptive, so that the robot can learn how each human user teachs and interacts. More info …

Self-organization, complex-systems and the evolution of language and speech

Human vocalization systems are characterized by complex structural properties. They are combinatorial, based on the systematic reuse of phonemes, and the set of repertoires in human languages is characterized by both strong statistical regularities – universals—and a great diversity. Besides, they are conventional codes culturally shared in each community of speakers. What is the origins of the forms of speech? What are the mechanisms that permitted their evolution in the course of phylogenesis and cultural evolution? How can a shared speech code be formed in a community of individuals?

Using computational modeling, I have been studying the way the concept of self-organization, and its interaction with natural selection, can throw light on these three questions. More info …

Modeling language acquisition in humans and robots

We investigate the mechanisms that enable humans and robots to learn new words and to use them in appropriate situations. We have built a number of robotic and computational experiments studying the mechanisms of concept formation, joint attention, social coordination and language games, and articulating the roles of learning, physical and environmental biases in language acquisition. The unifying theme of all these experiments is development: we explore the hypothesis that language can only be acquired through the progressive structuring of the sensorimotor and social experience. More info …

Emotional speech synthesis and emotional speech recognition

I have been working on algorithms for emotional speech synthesis. The objective was to manipulate the prosody of computer generated speech signals so that a human listener can perceive different kinds of emotions or attitudes, such as happiness, sadness or anger. The algorithms that I developped were inspired by psychoacoustic studies but in no way tryed to reproduce precisely the way humans modulate their prosody to express emotions. Rather, I developed operators for prosodic deformation which are analogous to the deformation of faces in Walt Disney pictures used to express visually the emotions of characters. In brief, there was little science in this project, but a lot of fun! More info...