{"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/gibson-env-real-world-perception-for-embodied","title":"Gibson Env: Real-World Perception for Embodied Agents","arxiv_id":"1808.10654","date":"2018-08-31","proceeding":"CVPR 2018 6","authors":["Fei Xia","Amir Zamir","Zhi-Yang He","Alexander Sax","Jitendra Malik","Silvio Savarese"],"abstract":"Developing visual perception models for active agents and sensorimotor\ncontrol are cumbersome to be done in the physical world, as existing algorithms\nare too slow to efficiently learn in real-time and robots are fragile and\ncostly. This has given rise to learning-in-simulation which consequently casts\na question on whether the results transfer to real-world. In this paper, we are\nconcerned with the problem of developing real-world perception for active\nagents, propose Gibson Virtual Environment for this purpose, and showcase\nsample perceptual tasks learned therein. Gibson is based on virtualizing real\nspaces, rather than using artificially designed ones, and currently includes\nover 1400 floor spaces from 572 full buildings. The main characteristics of\nGibson are: I. being from the real-world and reflecting its semantic\ncomplexity, II. having an internal synthesis mechanism, \"Goggles\", enabling\ndeploying the trained models in real-world without needing further domain\nadaptation, III. embodiment of agents and making them subject to constraints of\nphysics and space.","url_abs":"http://arxiv.org/abs/1808.10654v1","url_pdf":"http://arxiv.org/pdf/1808.10654v1.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":"gibson-env-real-world-perception-for-embodied","repo_url":"https://github.com/StanfordVL/GibsonEnv","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"gibson-env-real-world-perception-for-embodied","repo_url":"https://github.com/StanfordVL/GibsonSim2RealChallenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"gibson-env-real-world-perception-for-embodied","repo_url":"https://github.com/facebookresearch/fair_self_supervision_benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"gibson-env-real-world-perception-for-embodied","repo_url":"https://github.com/facebookresearch/habitat-challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"gibson-env-real-world-perception-for-embodied","repo_url":"https://github.com/vkartik97/habitat-challenge-two-docker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"general-reinforcement-learning","task_name":"General Reinforcement Learning"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"}],"methods":[],"datasets_introduced":[{"slug":"gibson-environment","name":"Gibson Environment","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1808.10654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.10654"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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