Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Overall Objectives

Overall Objectives

Can a machine learn like a child? Can it learn new skills and new knowledge in an unknown and changing environment? How can an embodied agent, e.g. a robot, discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop without the intervention of an engineer? What can it learn through natural social interactions with humans?

These are the questions that are being investigated in the FLOWERS research team at Inria Bordeaux Sud-Ouest and Ensta ParisTech. Rather than trying to imitate the intelligence of adult humans like in the field of Artificial Intelligence, we believe that trying to reconstruct the processes of development of the child's mind will allow for more adaptive, more robust and more versatile machines. This fundamental approach to the challenge of autonomous learning is called developmental robotics, or epigenetic robotics, and integrates concepts and theories from artificial intelligence, machine learning, neuroscience and developmental psychology. As many theories in neuroscience and developmental psychology are not formalized, this implies a crucial computational modeling activity, which in return provides means to assess the internal coherence of theories and sketch new hypothesis about the development of the human child's sensorimotor and cognitive abilities. Such computational modelling is also used as a foundational conceptual basis to build flexible lifelong autonomous machine learning systems.

Our team focuses in particular on the study of developmental constraints that allow for efficient open-ended learning of novel sensorimotor and interaction skills in embodied systems. In particular, we study constraints that guide exploration in large sensorimotor spaces:

We also study how these constraints on exploration can allow a machine to bootstrap multimodal perceptual abstractions associated to motor skills, in particular in the context of modelling language acquisition as a developmental process grounded in action.

Among the developmental principles that characterize human infants and can be used in developmental machines, FLOWERS focuses on the following three principles:

Research axis

The work of FLOWERS is organized around the following axis: