Papers
28
Total Citations
647
H-Index
13
About
Pablo Barros is a leading researcher in affective computing and human-robot interaction, whose work focuses on endowing robots with the ability to perceive, express, and learn from human emotions. His major contributions span deep learning architectures for multimodal emotion recognition, including sequence-dependent hierarchical features (83 citations) and cross-channel convolutional neural networks for emotional expression recognition (51 citations). Barros has pioneered methods for robots to recognize emotions not only from facial expressions but also from body language and gestures, as demonstrated in his work on emotion recognition from body expressions (30 citations) and real-time gesture recognition with deep neural architectures (46 citations). His research on the Neuro-Inspired COmpanion robot (NICO) has been particularly influential, exploring how robots can learn to associate and teach emotion expressions (31 citations). Barros has also advanced the field by investigating context-dependent personality traits in care robots (52 citations) and developing empathy-driven emotion expressions through affective modulations (30 citations). His recent work on affect-driven learning for collaborative human-robot interactions (29 citations) continues to push boundaries, making him a pivotal figure in creating socially intelligent robots capable of natural, emotionally-aware interactions.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Multichannel Convolutional Neural Network for Hand Posture Recognition77 citations · 2014
- 3Developing crossmodal expression recognition based on a deep neural model74 citations · 2016
- 4
- 5
- 6
- 7
- 8Emotion Recognition from Body Expressions with a Neural Network Architecture30 citations · 2017
- 9Learning Empathy-Driven Emotion Expressions using Affective Modulations30 citations · 2018
- 10