Papers
3
Total Citations
22
H-Index
3
About
Laurent Fitte-Duval is a researcher in human-robot interaction (HRI), with a focus on enabling robots to perceive and respond to human social cues. His work centers on situation assessment and social intelligence for robotic assistants, particularly through visual perception of the human body. Fitte-Duval’s major contributions include developing methods for upper body detection and body pose classification, as well as combining RGB-D features to classify head and upper body orientation—critical for a robot to understand a person’s intent to interact. His most cited paper, "Romeo2 Project: Humanoid Robot Assistant and Companion for Everyday Life: I. Situation Assessment for Social Intelligence" (2014, 12 citations), outlines a framework for social intelligence in the Romeo2 humanoid robot, targeting real-world assistance and companionship. This work, along with his evaluations of feature sets for pose classification (2015, 7 citations) and orientation estimation (2016, 3 citations), has informed the development of more intuitive and responsive robotic systems. Fitte-Duval’s research is foundational for creating robots that can naturally engage with people in everyday environments, bridging computer vision and social robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3