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About

Daiwei Lin is a researcher specializing in human-robot interaction, affective computing, and reinforcement learning, with a focus on developing autonomous systems capable of natural, engaging behavior in real-world environments. Their work addresses a critical challenge in robotics: enabling physical agents to move beyond controlled, one-to-one interactions and adapt dynamically to complex, multi-person settings such as public spaces. Among their most notable contributions is a series of studies exploring how deep reinforcement learning can be applied to teach robots to generate life-like, engaging behaviors autonomously. A particularly compelling field study conducted in a public museum demonstrated the real-world viability of these approaches, bridging the gap between laboratory research and practical deployment. Lin has also made meaningful strides in individualized human-machine interaction, proposing methods that allow robots to learn and tailor their movements to individual human preferences — a significant step toward truly personalized robotic systems. With a cumulative citation count across their key works reaching over 20 references, Lin's research is gaining recognition within the robotics and AI communities. Their contributions represent important groundwork for the next generation of responsive, user-centered autonomous systems.