Lucas Paletta
Papers
30
Total Citations
480
H-Index
14
About
Lucas Paletta is an Austrian researcher whose work spans robotics, computer vision, cognitive science, and assistive technologies — a rare interdisciplinary breadth that has earned him recognition across multiple scientific communities. His early contributions focused on visual perception for autonomous robotic systems, including omnidirectional imaging for robot navigation (38 citations) and object detection in challenging environments such as urban scenes and sewer inspection robots. A defining thread throughout his career is the application of Gibson's affordance theory to machine perception, producing influential frameworks for how robots learn to anticipate interaction opportunities through visual cues (39 citations). Paletta later turned his attention to human-robot interaction, developing probabilistic models of human attention and gaze analysis to measure situation awareness in real time — work that attracted over 33 citations per study and represents a meaningful advance in making HRI systems more adaptive and human-centered. Perhaps most notably, his research extended into healthcare, investigating socially assistive robots for dementia care, including a widely cited mixed-methods clinical protocol (35 citations) and qualitative studies on caregiver expectations (23 citations). This trajectory — from robot navigation to cognitive modeling to elder care — reflects a researcher driven by both technical rigor and genuine societal impact.
Research Focus
Key Achievements
Top Papers
- 1Learning Predictive Features in Affordance based Robotic Perception Systems39 citations · 2006
- 2Robust localization using context in omnidirectional imaging38 citations · 2002
- 3
- 4
- 5
- 6Visual Learning of Affordance Based Cues29 citations · 2006
- 7Visual object detection for autonomous sewer robots27 citations · 2003
- 8Urban Object Recognition from Informative Local Features26 citations · 2006
- 9
- 10