Papers
8
Total Citations
57
H-Index
4
About
Unai Zabala is a leading researcher in social robotics, specializing in human-robot interaction, non-verbal communication, and embodied storytelling. His work focuses on enabling robots to generate natural, expressive gestures—particularly beat gestures—that align with speech to build trust and engagement. Zabala’s major contributions include developing a Generative Adversarial Network (GAN) for automatic gesture generation and a hybrid system for storyteller robots that combines beats with emotional emphasis. His most cited paper, “Expressing Robot Personality through Talking Body Language” (2021, 29 citations), demonstrates how coherent body language enhances robot credibility. He also led the GidaBot project, deploying heterogeneous robot teams for multi-floor tour guiding. With over 55 total citations, Zabala’s research has advanced the field of social robotics by making interactions more intuitive and lifelike. His recent work explores integrating zero-shot large language models (LLMs) into collaborative storytelling with Pepper robots, pushing the boundaries of multimodal interaction. Zabala’s achievements underscore his commitment to creating robots that communicate not just with words, but with the nuanced body language that defines human connection.
Research Focus
Key Achievements
Top Papers
- 1Expressing Robot Personality through Talking Body Language29 citations · 2021
- 2Modeling and evaluating beat gestures for social robots11 citations · 2021
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- 6Learning to Gesticulate by Observation Using a Deep Generative Approach2 citations · 2019
- 7Can a Social Robot Learn to Gesticulate Just by Observing Humans?2 citations · 2020
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