Jens Hoefinghoff
Papers
5
Total Citations
27
H-Index
3
About
Jens Hoefinghoff’s research lies at the intersection of human-robot interaction, adaptive robotics, and accessible artificial intelligence. His work focuses on making robot companions more intuitive and customizable for non-expert users, particularly in caregiving contexts. A key contribution is his exploration of how humanlike learning abilities affect people’s perception and evaluation of robots—a study that has garnered 8 citations and reveals that more adaptive, humanlike robots can paradoxically overwhelm users. Hoefinghoff also pioneered adaptable robot companions for elderly users, demonstrating in a 7-cited study how seniors can successfully train robots to perform household tasks. His technical innovations include implementing a decision-making algorithm based on somatic markers on the Nao robot (6 citations), which allows robots to make context-aware choices without extensive reprogramming. Further, he developed a framework enabling non-experts to create custom robotic applications, such as playing card games with a companion robot. Hoefinghoff’s work is notable for bridging the gap between sophisticated AI decision-making and real-world usability, showing that effective robot companions must balance adaptability with simplicity.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5