Daniel Speck

Hamburg University of Technology, Universität Hamburg

Papers

Total Citations

H-Index

About

Daniel Speck is a researcher at the intersection of human-robot interaction (HRI) and computer vision, with key contributions in designing socially intelligent robotic systems. His work explores how robot personality traits influence human acceptance, as demonstrated in his highly cited 2019 study on personality-driven robots for interactive scenarios. In this work, Speck developed an autonomous AI system for a dice game HRI study, comparing socially engaged versus competitive robot personalities to determine which fosters greater user acceptance—a foundational insight for designing more intuitive social robots. His research also advances real-time perception, with a notable 2019 paper on ball localization using convolutional neural networks (CNNs), achieving 13 citations for its practical approach to enabling robots to track objects in dynamic environments. With over 28 combined citations across his most-cited works, Speck’s contributions bridge the gap between robotic social cognition and computer vision, offering valuable frameworks for developing robots that are both perceptually capable and socially attuned. His work is particularly relevant for students and researchers in HRI, AI, and autonomous systems, providing concrete methodologies for building robots that can engage naturally with humans.