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About

Axel Buendia’s research lies at the intersection of human-robot interaction, affective computing, and character animation, with a focus on making robots and virtual characters move in ways that feel natural, expressive, and socially intelligent. His major contributions include a landmark survey on emotion expression through body posture and movement, which systematically identifies the key motion factors that convey feelings like joy or sadness—a foundational resource for researchers in computer graphics and robotics. He also pioneered methods for evaluating human-likeness in robot movements using virtual reality, proposing criteria such as base inertia and velocity profile that have shaped how designers craft believable motion. His work on procedural locomotion enables multilegged characters to adapt their gait in real time to dynamic environments, advancing animation without reliance on motion capture. Buendia contributed to the Romeo2 project, a humanoid robot designed as a companion for everyday life, and developed hybrid decision-making systems for interactive person following, blending fuzzy logic with multi-objective optimization. With over 80 citations across his most-cited papers, his research continues to influence how machines perceive and replicate human movement, bridging engineering and psychology to create more intuitive social robots.