Develop novel algorithms for multi-agent reinforcement learning (MARL), enabling multiple autonomous assets to learn how to interact and collaborate towards a specified task.
This project will develop new machine learning inspired algorithms to extract, communicate and fuse information from networks of sensors, converting sensing into actionable information.
This project seeks to implement a dImplementation of differentiable physics engine for distributed motion planning in multi-agent loco-manipulation tasks.
The aim of this project is to enhance the coordination capabilities of autonomous agents during the deployment, either with other autonomous platforms or with human operators.
The project aims to develop new computational algorithms to optimize the acquisition and processing of event data (e.g., single-photon data) and enable fast imaging in extreme environmental conditions using statistical and learning based approaches.
In this work we will deliver highly original solutions to two important problems in our society, i.e., the physical limitations of data-storage platforms, and their booming energy demand.
Co-designing advanced CI algorithms and computing hardware to reconstruct images with a new regime of precision, robustness, efficiency, and scalablity, enabling in particular addressing low-resource setting applications, with a specific focus on low-SWAP platforms.
