Javier Sevilla Salcedo
Papers
6
Total Citations
31
H-Index
3
About
Javier Sevilla Salcedo is a leading researcher at the intersection of social robotics and natural language processing, whose work is redefining how robots communicate with humans. His primary research areas include human-robot interaction, deep learning for speech generation, and bio-inspired cognitive architectures. Sevilla Salcedo’s major contributions center on making social robots more natural and responsive conversation partners. His most cited paper, “Using Large Language Models to Shape Social Robots’ Speech” (2023, 11 citations), pioneered the integration of LLMs to move beyond predefined texts, enabling robots to generate dynamic, context-aware utterances. He further advanced the field with a bio-inspired endogenous attention architecture (2022, 6 citations) that allows robots to prioritize sensory inputs for richer environmental perception, and deep learning models for paraphrasing (2023, 6 citations) and co-speech gesture synchronization (2025, 2 citations). Notably, his work on remote communication for elderly users via social robotics (2021, 3 citations) demonstrates a commitment to socially impactful applications. With a growing citation record and publications in both English and Spanish, Sevilla Salcedo is shaping the next generation of socially intelligent robots that can engage in fluid, empathetic dialogue.
Research Focus
Key Achievements
Top Papers
- 1Using Large Language Models to Shape Social Robots’ Speech.11 citations · 2023
- 2
- 3Using Deep Learning for Implementing Paraphrasing in a Social Robot6 citations · 2023
- 4Comunicación remota entre familiares a través de la robótica social3 citations · 2021
- 5Modelos de lenguaje natural para robots sociales3 citations · 2022
- 6