MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
This addresses the need for better simulation tools for robotics and AI navigation research, though it is incremental as it builds on existing simulation and multimodal learning concepts.
The authors tackled the problem of developing multisensory models for goal-directed navigation in complex indoor environments by introducing MINOS, a simulator that benchmarks deep-learning methods and shows current deep reinforcement learning approaches fail in large realistic environments, with experiments indicating multimodality improves navigation in cluttered scenes.
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