Erik Billing
Papers
43
Total Citations
959
H-Index
16
About
Erik Billing is a prominent researcher in human-robot interaction (HRI), social robotics, and machine learning, whose work has made significant contributions to the intersection of robotics, therapy, and artificial intelligence. He is perhaps best known for his pioneering role in the DREAM project (Development of Robot-Enhanced therapy for children with Autism spectrum disorder), an EU-funded initiative that produced supervised autonomous robotic systems designed to improve social skills in children with autism spectrum disorder (ASD). His 2017 paper on building autonomous systems for robot-enhanced therapy has accumulated 138 citations, reflecting the field's enthusiasm for his clinical and technical innovations. Billing's research extends beyond therapeutic applications. His foundational 2010 work formalizing Learning from Demonstration provided the robotics community with a rigorous conceptual framework that continues to inform robot programming methodology. He has also explored affective touch in HRI — his study on emotional conveyance through tactile interaction with the Nao robot garnered 134 citations — and more recently integrated large language models like GPT-3 into human-robot dialogue systems, anticipating the transformative role of AI in robotics. Through datasets covering over 3,000 therapy sessions and consistent attention to user experience, Billing's work bridges technical innovation with meaningful real-world impact.
Research Focus
Key Achievements
Top Papers
- 1
- 2Affective Touch in Human–Robot Interaction: Conveying Emotion to the Nao Robot134 citations · 2017
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- 5A Formalism for Learning from Demonstration<sup>*</sup>59 citations · 2010
- 6
- 7Evaluating the User Experience of Human–Robot Interaction48 citations · 2020
- 8Language Models for Human-Robot Interaction42 citations · 2023
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- 10Behavior recognition for Learning from Demonstration27 citations · 2010