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About

Arun Ahuja is a researcher whose work sits at the intersection of reinforcement learning, embodied AI, and human-robot interaction, with a particular focus on creating intelligent agents capable of naturalistic, physically grounded behavior. His most influential contribution, "Catch & Carry" (2020, 98 citations), tackled one of animation and robotics' most persistent challenges: building flexible, physics-based humanoid controllers capable of diverse whole-body tasks involving object interactions — work with implications spanning graphics, motor neuroscience, and robotic control. Complementing this, his research on interactive intelligence, represented by "Imitating Interactive Intelligence" (2020, 43 citations) and its multimodal successor (2021, 32 citations), explores how artificial agents can be designed to interact naturally with humans through language and physical collaboration — an ambitious step toward the science-fiction vision of robots as genuine human companions. Across these projects, Ahuja consistently leverages imitation learning and self-supervised techniques to bridge the gap between rigid, task-specific controllers and genuinely adaptive agents. With nearly 190 cumulative citations across his key works, his research has made a meaningful mark on the growing field of embodied, multimodal AI.