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About

Arthur Brussee is a researcher working at the intersection of robotics, machine learning, and embodied artificial intelligence, with a particular focus on sim-to-real transfer, multimodal interaction, and imitation learning. His work addresses one of robotics' most persistent challenges: enabling agents to operate intelligently in complex, real-world environments by learning from human behavior and simulation. Brussee's most notable contribution, "NeRF2Real" (2023, 43 citations), demonstrates a compelling pipeline for transferring vision-guided bipedal locomotion skills from simulation to reality using Neural Radiance Fields, allowing robots to navigate real scenes captured with nothing more than a phone camera. This work significantly advances the practicality of sim2real methods for physically demanding tasks requiring active perception. Alongside this, his contributions to "Imitating Interactive Intelligence" (2020, 43 citations) and its follow-up on multimodal interactive agents (2021, 32 citations) reflect a broader ambition: creating agents that can perceive, communicate, and collaborate with humans naturally. These papers collectively point toward a research vision where robots don't just move intelligently, but genuinely interact with the people and environments around them — a goal as ambitious as it is consequential.