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About

Thomas Sievers is an emerging researcher specializing in human-robot interaction (HRI), social robotics, and the integration of artificial intelligence into humanoid service robots. His work sits at a compelling intersection of cognitive architectures, natural language processing, and practical robot deployment, making meaningful contributions to how robots perceive, communicate with, and respond to humans in real-world settings. Sievers has developed accessible frameworks for connecting sophisticated AI technologies — including Large Language Models and Vision-Language Models — to humanoid robots without requiring deep technical expertise from end users. His most-cited work (2023, 8 citations) exemplifies this practical philosophy. He has explored nuanced dimensions of robot communication, including the social effects of regional language use, emotion recognition via ChatGPT, and the often-overlooked challenge of interpreting conversational pauses during turn-taking. Beyond laboratory research, Sievers investigates real-world deployment scenarios, from municipal public service offices to school classrooms, demonstrating a commitment to socially relevant applications. His 2025 work on memory retrieval through cognitive architectures signals growing ambitions in enabling robots with more human-like cognitive capabilities. With a growing citation record across just a few years, Sievers represents a promising voice in the future of socially intelligent robotics.