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Bioinspired Stimulus Selection Under Multisensory Overload in Social Robots Using Reinforcement Learning.

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Predicción y sincronización de gestos de co-habla para mejorar las interacciones humano-robot utilizando modelos de

Enrique Fernández-Rodicio1, Christian Dondrup2, Javier Sevilla-Salcedo1

  • 1Department of Systems Engineering and Automation, University Carlos III of Madrid, Av. de la Universidad, 30, 28911 Leganés, Spain.

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Resumen

Este estudio presenta un sistema novedoso para que los robots generen habla y gestos sincronizados, mejorando la interacción humano-robot. El modelo de aprendizaje profundo predice y alinea eficazmente las señales no verbales con el lenguaje hablado para una comunicación más natural.

Palabras clave:
gestos de co-hablaaprendizaje profundopredicción de gestosinteracción humano-robotmodelos transformer

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Área de la Ciencia:

  • Robótica
  • Interacción Humano-Computadora
  • Inteligencia Artificial

Sus antecedentes:

  • Los robots realizan cada vez más tareas que requieren interacción humana, lo que exige que se les perciba como socios adecuados.
  • La apariencia animada del robot, lograda a través de expresiones y gestos, es crucial para la aceptación del usuario.
  • La sincronización del habla y los gestos del robot presenta un desafío importante para la comunicación natural.

Objetivo del estudio:

  • Desarrollar un sistema que prediga y sincronice los gestos del robot con su habla.
  • Permitir a los robots generar gestos de co-habla que apoyen la comunicación verbal.
  • Mejorar la interacción humano-robot a través de una expresividad no verbal mejorada.

Principales métodos:

  • Se empleó un modelo de predicción basado en aprendizaje profundo para etiquetar el habla del robot con tipos de expresión apropiados.
  • Se desarrolló un módulo de sincronización basado en reglas para alinear los gestos predichos con segmentos de habla específicos.
  • Se evaluaron dos enfoques distintos: redes neuronales recurrentes con campos aleatorios condicionales y modelos transformer.

Principales resultados:

  • El sistema desarrollado predice y sincroniza con éxito los gestos con el habla del robot.
  • El sistema demuestra la capacidad de seleccionar gestos de co-habla apropiados dentro de las restricciones de interacción en tiempo real.
  • Ambas arquitecturas de aprendizaje profundo probadas resultaron efectivas en la predicción y sincronización de gestos.

Conclusiones:

  • El sistema propuesto mejora la expresividad del robot generando habla y gestos sincronizados.
  • Este avance contribuye a una comunicación humano-robot más natural y efectiva.
  • La investigación valida el uso del aprendizaje profundo para crear robots con capacidades de interacción social mejoradas.