The goal of this thesis is to investigate how large language models (LLMs) can be utilized to generate context-aware procedural behaviors for avatars in human‑centric simulation environments. In simulation environments where robots interact with avatars and virtual agents, LLMs can serve as high‑level reasoning modules that translate natural‑language context into structured action plans. These plans govern how virtual agents behave, interact, and adapt to dynamic environments, supporting workflows such as task coordination, object manipulation, or situational response. Traditional systems rely on manually scripted rules or stochastic animation graphs, which are often rigid and difficult to scale. This thesis explores whether LLM‑guided high‑level planning can provide a more flexible, semantically grounded, and scalable alternative.
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#J-18808-LjbffrTyp:
VollzeitArbeitsmodell:
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Erfahrung:
2+ yearsArbeitsverhältnis:
AngestelltVeröffentlichungsdatum:
17 Nov 2025Standort:
WorkFromHome
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