Our goal is to achieve faster and more accurate convergence by reducing the computational burden and providing strict guarantees on the resulting optimal policy. We will then incorporate global strategies to escape local minima and enable planning across various contact modes (e.g., due to intermittent contact interactions). We will further explore the integration of multimodal sensory information, including force and touch detection, leveraging recent results with foundation models. These enhancements will increase the robustness and safety of our policies during physical interactions, enabling reliable robotic manipulation and locomotion in complex environments.
Our team's unique experimental capabilities and extensive expertise in reinforcement learning and optimal control will enable the establishment of a comprehensive framework for the optimization and control of robotics policies, ultimately enabling the development of robots capable of complex, adaptive, and reliable movements deployed in real-world applications.
The chair will benefit from strong interactions with the synergy chair C3PO, as well as other chairs in numerical optimization and machine learning. Results will be translated into practical problems through an effective collaboration with the robotics manufacturer PAL and the end-user AIRBUS. The project will also consider direct socioeconomic impacts, with a particular focus on direct outreach to students through the creation of an international exchange program and to young audiences through specific robotics activities.