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Página principal > Libros y colecciones > Capítulos de libros > End-to-end driving via conditional imitation learning |
Publicación: | Institute of Electrical and Electronics Engineers (IEEE), cop.2018 |
Descripción: | 8 pàg. |
Resumen: | Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. |
Ayudas: | Agencia Estatal de Investigación TIN2017-88709-R Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/FI-B1-00162 |
Derechos: | Tots els drets reservats. |
Lengua: | Anglès |
Documento: | Capítol de llibre ; recerca ; Versió acceptada per publicar |
Materia: | Robot sensing systems ; Task analysis ; Vehicles ; Cameras ; Roads ; Navigation |
Publicado en: | 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, p. 4693-4700, ISBN 978-1-5386-3081-5 |
Postprint 9 p, 5.3 MB |