Generative artificial intelligence algorithms for understanding and countering online polarization

Digital platforms enable the widespread dissemination of information, but their engagement-centric business models often promote the spread of ideologically homogeneous or controversial political content. These models can lead to the polarization of political opinions and impede the healthy functioning of democratic systems. The PhD will investigate innovative generative AI models devised for a deep understanding of political polarization and for countering its effects. It will mobilize several areas of AI: generative learning, frugal AI, continual learning, and multimedia learning. Advances will be associated with the following challenges:
-the modeling of political polarization, and the translation of the obtained domain model into actionable implementation requirements that will be used as inputs of AI algorithms;
-the curation of massive and diversified multimodal political data to ensure topical and temporal coverage, and to map these data to a common semantic representation space;
-the training of politics-oriented generative models to encode relevant knowledge effectively and efficiently and to generate labeled training data for downstream tasks;
-the specialization of the models for the specific tasks needed for a fine-grained understanding of polarization (topic detection, entity recognition, sentiment analysis);
-the continual update of the politics-oriented generative models and polarization-specific tasks to keep pace with the evolution of political events and news.

High mobility mobile manipulator control in a dynamic context

The development of mobile manipulators capable of adapting to new conditions is a major step forward in the development of new means of production, whether for industrial or agricultural applications. Such technologies enable repetitive tasks to be carried out with precision and without the constraints of limited workspace. Nevertheless, the efficiency of such robots depends on their adaptation to the variability of the evolutionary context and the task to be performed. This thesis therefore proposes to design mechanisms for adapting the sensory-motor behaviors of this type of robot, in order to ensure that their actions are appropriate to the situation. It envisages extending the reconfiguration capabilities of perception and control approaches through the contribution of Artificial Intelligence, here understood in the sense of deep learning. The aim is to develop new decision-making architectures capable of optimizing robotic behaviors for mobile handling in changing contexts (notably indoor-outdoor), and for carrying out a range of precision tasks.

Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

Fine-grained dexterous manipulation presents significant challenges for robots due to the need for precise object handling, coordination of contact forces, and utilization of visual observations. This research aims to address these challenges by investigating the integration of vision and kinesthetic sensors, sim2real techniques, and generalization through embodiment. The objective is to develop end-to-end algorithms and models that enable robots to manipulate objects with exceptional precision and adaptability. The research will focus on learning from large-scale data, transferring knowledge from simulations to real-world scenarios, and efficiently generalizing through low-shot fine-tuning.