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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Aprendizaje de la manipulación de todo el cuerpo rica en contacto con aprendizaje de refuerzo guiado por el ejemplo
Jose A Barreiros1, Aykut Özgün Önol1, Mengchao Zhang1
1Toyota Research Institute, Cambridge, MA, USA.
Science robotics
|August 20, 2025
Resumen
Los robots ahora pueden aprender habilidades de manipulación de todo el cuerpo para objetos grandes utilizando el aprendizaje por refuerzo guiado por el ejemplo. Este enfoque permite a los robots realizar tareas complejas utilizando retroalimentación táctil y propioceptiva, incluso sin seguimiento visual.
Área de la Ciencia:
- La robótica
- Inteligencia artificial
- Interacción hombre-robot
Sus antecedentes:
- La manipulación humana implica movimientos complejos de todo el cuerpo y el cumplimiento de la piel / músculo para la estabilidad.
- La manipulación robótica se enfrenta a desafíos con escenarios ricos en contacto debido a la complejidad combinatoria.
- Los métodos existentes luchan con el razonamiento explícito para las extensas interacciones robot-objeto.
Objetivo del estudio:
- Desarrollar habilidades robustas de manipulación de todo el cuerpo para robots utilizando el aprendizaje por refuerzo guiado por el ejemplo.
- Para abordar los desafíos de los comportamientos ricos en contacto en la manipulación robótica.
- Permitir a los robots manipular objetos grandes y difíciles de manejar con eficacia.
Principales métodos:
- Aprendizaje de refuerzo guiado por el ejemplo para la generación de habilidades.
- Utilizó un robot humanoide con piel deformable y sensible a la presión (robot Punyo).
- Entrenamiento realizado en simulación con aleatorización de dominio para la transferencia de simulación a real.
Principales resultados:
- Políticas generadas con éxito para la manipulación de todo el cuerpo de objetos cotidianos (jarro de agua, cajas).
- Se ha demostrado la destreza de la manipulación a ciegas utilizando sólo la retroalimentación proprioceptiva y táctil.
- Se logró una transferencia robusta de políticas al hardware debido al cumplimiento del robot y la aleatorización del dominio.
Conclusiones:
- El aprendizaje por refuerzo guiado por el ejemplo es efectivo para enseñar a los robots la manipulación de todo el cuerpo.
- El cumplimiento del robot es crítico para la manipulación exitosa de todo el cuerpo en robots humanoides.
- Los métodos desarrollados permiten a los robots realizar tareas de manipulación complejas con un mínimo de detección.
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