Research ZeilingerOpen OpportunitiesIn this project, we want to explore possible extensions of predictive control barrier functions to the multi-agent setting. Predictive control barrier functions [1] allow certifying safety of a system in terms of constraint satisfaction and provide stability guarantees with respect to the set of safe states in case of initial feasibility. This allows augmenting any human or learning-based controller with closed-loop guarantees through a so-called safety filter [2] which is agnostic to the primary control objective. As current formulations are restricted to single agents, the goal is to investigate how this formulation can be extended for multi-agent applications and how the interactions between the agents can be exploited in order to reduce computational overhead. - Engineering and Technology, Systems Theory and Control
- Master Thesis
| The development of Large Language Models (LLMs), like ChatGPT and GPT-4, has influenced
the field of Natural Language Processing and Artificial Intelligence with their exceptional proficiency in comprehending and generating language, alongside their notable generalization and reasoning abilities. Consequently, recent research efforts have focused on leveraging the capabilities of LLMs to improve recommender systems. Recommender systems significantly influence human behavior by shaping users’ preferences, decision-making processes, and overall engagement with digital content. This project develops on the interpretation of recommender systems (controller) in feedback interaction with the users (system), [3]. By following a similar approach to [2], we will investigate how a careful integration of a LLM with a Model Predictive Control (MPC) framework can enhance recommender systems by ensuring accurate and adaptable recommendations while considering user preferences and constraints.
Understanding the influence of recommender systems over users behaviour and managing it effectively will be enhanced through the MPC framework, which offers a structured and interpretable approach to recommendation optimization. - Automotive Engineering, Computer Communications Networks, Electrical Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| In this project, we want to explore the application of predictive stability filters for automotive applications. Predictive stability filters allow augmenting human or learning-based controllers such that safety in terms of constraint satisfaction as well as stability of a desired setpoint can be guaranteed. Such algorithms present possible solutions for automotive applications such as, e.g., lane keeping. - Engineering and Technology, Systems Theory and Control
- Master Thesis
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