Papers
9
Total Citations
144
H-Index
6
About
Ely Repiso is a leading researcher in human-robot interaction, specializing in social robot navigation for approaching and accompanying people. His work focuses on developing adaptive, human-like behaviors that allow robots to move naturally alongside individuals and groups. Repiso’s key contributions include models for side-by-side and V-formation accompaniment, enabling robots to adapt their positioning and movement based on group size and dynamics—mimicking real pedestrian behavior. His most-cited paper, “Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People” (2019), has garnered 58 citations, while his subsequent work on group accompaniment (2020) has 29 citations. These studies have laid the groundwork for robots that can seamlessly join and navigate with people in real-life environments. Repiso’s research also addresses path planning using G²-splines and extended social force models, and he has developed metrics for evaluating human-robot collaborative navigation tasks. His notable achievement includes advancing Collaborative AI for social robots, a field he argues is essential for future daily human-robot collaboration. Through his iterative, real-world tested models, Repiso is helping to make social robots more intuitive, safe, and effective companions in shared spaces.
Research Focus
Key Achievements
Top Papers
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- 3Robot Approaching and Engaging People in a Human-Robot Companion Framework18 citations · 2018
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- 9Collaborative-AI: Social robots accompanying and approaching people2 citations · 2020