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About

Florian Golemo is a researcher specializing in robot learning, simulation-to-real transfer (Sim2Real), and multimodal environments for artificial agents. His work addresses one of robotics' most fundamental challenges: bridging the gap between simulated training environments and real-world deployment. His most cited contribution, "Sim2Real in Robotics and Automation: Applications and Challenges" (2021, 151 citations), establishes a comprehensive framework for understanding how simulation can underpin reliable large-scale automation. Complementing this, his development of HoME — a Household Multimodal Environment integrating over 45,000 3D house layouts — provided the research community with a richly diverse testbed for multi-sensory agent learning, earning 80 citations since 2017. Golemo has made notable methodological contributions through Active Domain Randomization and neural-augmented robot simulation, advancing how agents generalize across environment variations. His work on multi-agent trajectory prediction using Latent Variable Sequential Set Transformers further demonstrates his breadth, tackling safe autonomous navigation in socially complex settings. His doctoral thesis synthesized many of these threads into practical training pipelines for real robots. With over 370 cumulative citations, Golemo's research has meaningfully shaped how the robotics community approaches the sim-to-real transfer problem and scalable robot training.