Interactive Simulation

Interactive simulation aims to create realistic virtual environments for testing and training autonomous systems, particularly robots and AI agents, by modeling complex physical and behavioral interactions. Current research emphasizes leveraging large language models (LLMs) and graph neural networks (GNNs) to generate diverse and scalable simulation data, often incorporating techniques like reinforcement learning and digital twinning for improved realism and efficiency. This field is crucial for advancing autonomous systems across various domains, from robotics and autonomous driving to healthcare and education, by providing safer, cheaper, and more efficient methods for development and testing.