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Learning world models for advanced autonomous agent


Thesis topic details

General information

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centers spread throughout France
  

Reference

SL-DRT-25-0644  

Direction

DRT

Thesis topic details

Category

Technological challenges

Thesis topics

Learning world models for advanced autonomous agent

Contract

Thèse

Job description

World models are internal representations of the external environment that an agent can use to interact with the real world. They are essential for understanding the physics that govern real-world dynamics, making predictions, and planning long-horizon actions. World models can be used to simulate real-world interactions and enhance the interpretability and explainability of an agent's behavior within this environment, making them key components for advanced autonomous agent models.
Nevertheless, building an accurate world model remains challenging. The goal of this PhD is to develop methodology to learn world models and study their use in the context of autonomous driving, particularly for motion forecasting and developing autonomous agents for navigation.

University / doctoral school

Sciences et Technologies de l’Information et de la Communication (STIC)
Paris-Saclay

Thesis topic location

Site

Saclay

Requester

Position start date

01/10/2024

Person to be contacted by the applicant

RABARISOA Jaonary jaonary.rabarisoa@cea.fr
CEA
DRT/DIASI//LVA
CEA Saclay - Nano-INNOV
Bat 861 - PC 173 - F91191 Gif Sur Yvette Cedex
France
00169080129

Tutor / Responsible thesis director

PHAM Quoc Cuong quoc-cuong.pham@cea.fr
CEA
DRT/DIASI/SIALV/LVA
CEA SACLAY - Nano-INNOV
Bât. 861 - Point courrier 173
91191 Gif-sur-Yvette Cedex
0169082716

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