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About

Daniel Tanneberg is a robotics and artificial intelligence researcher whose work spans two compelling frontiers: neurobiologically inspired learning systems and intelligent human-robot interaction. Early in his career, Tanneberg made significant contributions to spiking neural network research, proposing recurrent architectures capable of planning as probabilistic inference — work that earned 54 citations and demonstrated that brain-inspired computation could solve complex sequential decision-making tasks. His subsequent investigations into stochastic recurrent networks and intrinsic motivation signals (23 citations) further established his expertise in biologically plausible, adaptive learning for autonomous robots. More recently, Tanneberg has emerged as a leading voice in applying large language models to robotics. His 2024 paper LaMI (64 citations) introduced a groundbreaking LLM-based framework for multi-modal human-robot interaction, dramatically simplifying systems that traditionally required elaborate hand-crafted pipelines. Complementary work on CoPAL (25 citations) tackled corrective planning in open-world environments, while research on augmented reality-based robot training and gaze-driven intention estimation reflects his broader commitment to making robots accessible, explainable, and genuinely collaborative. Across more than 240 cumulative citations, Tanneberg's research consistently bridges theoretical innovation with practical, human-centered robotic systems.