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FLOWERS Research team

Flowing Epigenetic Robots and Systems

Team presentation

The Flowers team studies computational mechanisms allowing robots and humans to acquire open-ended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also called curiosity-driven active learning), autonomous unsupervised exploration, imitation and social learning, multimodal statistical inference, embodiment and maturation and self-organization.

The team considers cognitive development as a complex dynamical system which needs to be understood through systemic thinking, leveraging tools and concepts from computational sciences (artificial intelligence, machine learning and robotics), neuroscience and psychology. In this perspective, algorithms and robotics models are powerful scientific languages to express theories of cognitive development in the living.

Of particular interest to the Flowers team is the formation of repertoires of sensorimotor and interaction skills as well as their relation with the acquisition and evolution of languages.

The team is also working on applications of this research in three fields: adaptive human-computer interfaces, educational technologies and open-source robotics for art and education.

Research themes

The work of FLOWERS is organized around the following three axis:

  • Intrinsically motivated multitask learning and exploration, information seeking and active learning, including artificial curiosity;
  • Social learning, e.g. learning by imitation or demonstration, which implies both issues related to machine learning and human-robot interaction;
  • Mechanisms for learning to sequence and compose actions to reach goals, especially within the framework or reinforcement learning;
  • The role of embodiment, in particular through the concept of morphological computation, as well as the structure of motor primitives/muscle synegies that can leverage the properties of morphology and physics;
  • Maturational constraints which can allow the progressive release of novel sensorimotor degrees of freedom to be explored;

These research axes are applied to the learning of two kinds of skills: basic sensorimotor skills and basic socio-linguistic skills (bootstrapping and learning of the first words).

Keywords: Developmental and social robotics Situated and embodied cognition Artificial intelligence Exploration Learning Intrinsic motivation Natural human-robot interaction.

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