Exploring Large Language Model-Driven Agents for Environment-Aware Spatial Interactions and Conversations in Virtual Reality Role-Play Scenarios

Recent research has begun adopting Large Language Model (LLM) agents to enhance Virtual Reality (VR) interactions, creating immersive chatbot experiences. However, while current studies focus on generating dialogue from user speech inputs, their abilities to generate richer experiences based on the perception of LLM agents’ VR environments and interaction cues remain unexplored. Hence, in this work, we propose an approach that enables LLM agents to perceive virtual environments and generate environment-aware interactions and conversations for an embodied human-AI interaction experience in VR environments. Here, we define a schema for describing VR environments and their interactions through text prompts. We evaluate the performance of our method through five role-play scenarios created using our approach in a study with 14 participants. The findings discuss the opportunities and challenges of our proposed approach for developing environment-aware LLM agents that facilitate spatial interactions and conversations within VR role-play scenarios.