{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/minos-multimodal-indoor-simulator-for","title":"MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments","arxiv_id":"1712.03931","date":"2017-12-11","proceeding":null,"authors":["Manolis Savva","Angel X. Chang","Alexey Dosovitskiy","Thomas Funkhouser","Vladlen Koltun"],"abstract":"We present MINOS, a simulator designed to support the development of\nmultisensory models for goal-directed navigation in complex indoor\nenvironments. The simulator leverages large datasets of complex 3D environments\nand supports flexible configuration of multimodal sensor suites. We use MINOS\nto benchmark deep-learning-based navigation methods, to analyze the influence\nof environmental complexity on navigation performance, and to carry out a\ncontrolled study of multimodality in sensorimotor learning. The experiments\nshow that current deep reinforcement learning approaches fail in large\nrealistic environments. The experiments also indicate that multimodality is\nbeneficial in learning to navigate cluttered scenes. MINOS is released\nopen-source to the research community at http://minosworld.org . A video that\nshows MINOS can be found at https://youtu.be/c0mL9K64q84","url_abs":"http://arxiv.org/abs/1712.03931v1","url_pdf":"http://arxiv.org/pdf/1712.03931v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"minos-multimodal-indoor-simulator-for","repo_url":"https://github.com/ZhuFengdaaa/minos","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"minos-multimodal-indoor-simulator-for","repo_url":"https://github.com/minosworld/minos","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[{"slug":"minos","name":"MINOS","full_name":"MINOS"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.03931","atlas_url":"https://app.syntology.ai/?focus=1712.03931","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}