Luca Celotti

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About

Luca Celotti is a researcher at the forefront of embodied AI and multimodal machine learning, with a focus on creating realistic, interactive environments for artificial agents. His most influential work introduces HoME (a Household Multimodal Environment), a landmark platform that integrates vision, audio, semantics, physics, and object interaction within over 45,000 diverse 3D house layouts derived from the SUNCG dataset. This contribution, cited over 80 times, provides a crucial testbed for training agents to perceive and act in complex, human-like settings, bridging the gap between simulated learning and real-world application. By enabling agents to learn from multiple sensory streams simultaneously, Celotti’s work advances the development of more robust, context-aware AI systems. His research is essential reading for students and scholars interested in embodied cognition, multimodal perception, and the next generation of intelligent, interactive agents.