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Carreras de drones a nivel de campeón utilizando el aprendizaje por refuerzo profundo
Elia Kaufmann1, Leonard Bauersfeld2, Antonio Loquercio2
1Robotics and Perception Group, University of Zurich, Zürich, Switzerland. ekaufmann@ifi.uzh.ch.
Nature
|August 30, 2023
Resumen
Swift, un sistema autónomo, logró el rendimiento del campeón mundial humano en carreras de drones al combinar el aprendizaje de refuerzo profundo con datos del mundo real. Este sistema de IA ganó carreras de cabeza a cabeza, demostrando un nuevo hito para la robótica móvil autónoma.
Área de la Ciencia:
- La robótica
- Inteligencia artificial
- Aprendizaje automático
Sus antecedentes:
- Las carreras de drones en primera persona (FPV) requieren pilotaje de alta velocidad y navegación precisa.
- Los drones autónomos se enfrentan a desafíos al operar en límites físicos utilizando solo sensores a bordo.
Objetivo del estudio:
- Desarrollar un sistema autónomo capaz de competir al nivel de los campeones mundiales humanos en las carreras de drones FPV.
- Para demostrar la viabilidad de la IA avanzada en alta velocidad, la navegación robótica limitada a los sensores.
Principales métodos:
- El sistema Swift integra el aprendizaje por refuerzo profundo (RL) entrenado en simulación.
- Los datos de vuelo del mundo real se incorporaron para mejorar el rendimiento del modelo RL.
- El sistema autónomo fue probado en carreras cara a cara contra pilotos humanos profesionales.
Principales resultados:
- Swift demostró un rendimiento competitivo contra los campeones mundiales humanos en carreras del mundo real.
- El sistema autónomo logró el tiempo de carrera más rápido registrado.
- Swift ganó varias carreras contra competidores humanos de élite.
Conclusiones:
- Swift representa un avance significativo en la robótica móvil autónoma y la inteligencia de las máquinas.
- Los enfoques de aprendizaje híbrido que combinan simulación y datos del mundo real son efectivos para tareas robóticas complejas.
- Esta investigación allana el camino para el despliegue de IA avanzada en otros sistemas físicos dinámicos.
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