ChairesIA_2019_1 - Chaires de recherche et d'enseignement en Intelligence Artificielle - vague 1 de l'édition 2019

DeepCuriosity: Curiosity-driven exploration and curriculum learning in AI with applications to autonomous agents, automated discovery and educational technologies. – DeepCuriosity

Submission summary

The research vision and program of the DeepCuriosity project aim at developing the foundations of a new scientific approach to autonomous artificial intelligence and lifelong machine learning. While deep reinforcement learning has achieved impressive results recently (e.g. in complex board or video games), it is currently reaching both conceptual and practical limits. In particular, current AI systems are far from autonomous: they are still task specific; they require a lot of data and energy; they also require the intervention of engineers for each new task. To go beyond these limits, the approach proposed in this project is grounded in a decade of interdisciplinary work achieved by PY Oudeyer and his team at Inria Bordeaux Sud-Ouest, modelling mechanisms of infant learning and development in humans, in particular curiosity-driven learning. This has enabled to pose the first bricks of a new machine learning framework: intrinsically motivated goal exploration processes (IMGEPs). In IMGEPs, machines learn autonomously open repertoires of skills by self-supervised acquisition of world models, through sampling their own goals with curiosity-driven self-organized curriculum learning. These algorithms were shown to enable real world robots to learn efficiently repertoires of high-dimensional skills while being able to adapt quickly to changes in the environment, and under limited time and energy resources.

There are still fundamental challenges and limits to address to scale up these fundamental research advances, as well as their application in societally important domains. This project aims to address these challenges along the following “core AI research” and “application” objectives:

Core AI research objectives:
• We will develop novel self-supervised machine learning algorithms enabling curiosity-driven incremental learning of structured representations of goal/task spaces (starting from low-level high-dimensional perception).
• We will study how IMGEP algorithms can automate curriculum learning driven by learning progress in high-dimensions, and be applied to a wide diversity of machine and human learners.
• We will extend IMGEP algorithms to the context of human-robot collaboration through natural language interaction by 1) enabling the human to guide a curiosity-driven exploring robot for new tasks/environments using natural language instructions that the robot has learnt to understand; 2) enabling the robot to report what it does and sees using learnt natural language.

Applications objectives:
Recent proof-of-concepts showed how these general fundamental methods can find applications in the following three societally important fields, which we aim to scale up in DeepCuriosity leveraging dedicated collaborations with application-specific partners:
• Autonomous agents in large open video games world and autonomous robot exploration (this will leverage collaborations with the video games industry and French public defense organization).
• Robotized automated discovery of novel patterns in self-organized bio-printed cells systems, opening ground-breaking health application for people that need personalized tissue transplants (collaboration with a bio-printing company).
• Personalized curriculum of exercises for human learners in digital educational apps (collaboration with Académie de Bordeaux and EdTech companies).

DeepCuriosity will be key to empower the development of internationally impactful and visible AI research at Flowers, Inria Bordeaux and Région Nouvelle Aquitaine, promoting an interdisciplinary and human-centered approach to AI. It will boost a rich ecosystem of collaborations with other public research and educational institutions, and companies, addressing key societal issues (AI promoting inclusivity and diversity in edTech; AI with limited environmental footprint; health with improved automatization of tissue bio-printing). It will also develop new training courses covering these advances.

Project coordinator

Monsieur Pierre-Yves Oudeyer (Centre de Recherche Inria Bordeaux - Sud-Ouest)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

Inria FLOWERS Centre de Recherche Inria Bordeaux - Sud-Ouest

Help of the ANR 599,970 euros
Beginning and duration of the scientific project: May 2020 - 48 Months

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