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
| Natural Language Algorithms and Large Language Models, exemplified by GPT-4, have shown remarkable prowess across diverse domains. However, achieving human-like communication with robots remains a challenge. This project addresses the gap by enhancing the interface between natural language algorithms and robotic systems. Utilizing an existing chatGPT-based interface, we aim to introduce a vision component for dynamic environmental adaptation and employ it to assess task success. This metric, in turn, will fine-tune the language algorithm using reinforcement learning. The project's goal is a real-world demonstration of these advancements in a robotic manipulator, marking a significant stride towards more autonomous systems and sophisticated artificial intelligence. - Mechanical Engineering
- Master Thesis, Semester Project
| Conventional engineered control systems are typically designed for isolated and well-specified environments, emphasizing conservatism against uncertainty, especially in safety-critical domains with limited human interaction. In the pursuit of versatile, autonomous systems capable of adapting to diverse environments, there is a growing need for effective communication through natural language. Despite the successes of Natural Language algorithms and Large Language Models like GPT-4, leveraging these models for instructing autonomous safety-critical control systems remains an open question. This project addresses this gap by building upon a preliminary control architecture, translating natural language commands into Model Predictive Control (MPC) formulations. By utilizing the safety and stability assurances of MPC, we aim to automate the generation of optimization problems, providing non-conservative feasibility. Additionally, the project explores the use of predictive safety filters to mitigate uncertainty introduced by language models, enhancing the reliability of autonomous systems. - Mechanical Engineering
- Master Thesis, Semester Project
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