Learning Visual Representations with Caption Annotations

Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.

1. Difference in female/male salary: 34/40 points

2. Difference in salary increases female/male: 35/35 points

3. Salary increases upon return from maternity leave: Non calculable

4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points

The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.

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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100

Détail des indicateurs :

1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points

2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points

3. Toutes les salariées augmentées revenant de congé maternité : Incalculable

4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points

Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.

Perception to help robots understand and interact with the environment. 

Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robot understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments.

Equip robots to interact safely with humans, other robots and systems.

For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet.

Providing embodied agents with sequential decision-making capabilities to safely execute complex tasks in dynamic environments. 

To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve  robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.