PrincetonComputer SciencePIXL GroupPublications → [Savva et al. 2017] Local Access
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

arXiv preprint, December 2017

Manolis Savva, Angel X. Chang, Alexey Dosovitskiy,
Thomas Funkhouser, Vladlen Koltun
Abstract

We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at http://minosworld.org . A video that shows MINOS can be found at https://youtu.be/c0mL9K64q84
Citation

Manolis Savva, Angel X. Chang, Alexey Dosovitskiy, Thomas Funkhouser, and Vladlen Koltun.
"MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments."
arXiv:1712.03931, December 2017.

BibTeX

@techreport{Savva:2017:MMI,
   author = "Manolis Savva and Angel X. Chang and Alexey Dosovitskiy and Thomas
      Funkhouser and Vladlen Koltun",
   title = "{MINOS}: Multimodal Indoor Simulator for Navigation in Complex
      Environments",
   institution = "arXiv preprint",
   year = "2017",
   month = dec,
   number = "1712.03931"
}