{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "title": "AI2-THOR: an interactive indoor simulation interface for visual AI", "summary": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform.", "boundary": "The room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.", "area": "Embodied-AI simulation infrastructure", "topic": "Artificial intelligence", "year": 2017, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "target": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "humans": [ "Eric Kolve", "Roozbeh Mottaghi", "Daniel Gordon", "Yuke Zhu", "Abhinav Gupta", "Ali Farhadi" ], "authors_complete": true, "human_role_note": "Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.", "agents": [], "related": [], "subresults": [], "scientific_assessment": { "source_review": "SOURCE_CURATED", "scientific_review": "NOT_REVIEWED_BY_WRS", "formal_replay": "NOT_RUN", "provider_attestation": "NONE" }, "sources": [ { "id": "cur20261017-arxiv-1712-05474v1", "title": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1", "date": "2017-12-14" } ], "points": "150000", "published_grade": { "id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "entry_id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "band": "A", "modifier": "+", "target": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "what_changed": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform.", "scope": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform. The room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.", "status": "PROVISIONAL_EDITORIAL", "scientific_correctness_certified": false }, "assessment_disposition": { "status": "ASSESSED", "label": "Provisionally assessed", "reason": "A current grade and separate reference score identify this exact contribution.", "target": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "edition": "ratings-e039106f30dec177" } }, "version": { "kind": "PUBLIC_RECORD_VIEW", "previous": null, "published_at": "2026-10-23T00:00:00Z", "publisher": { "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33", "role": "WRS_EDITORIAL_PUBLICATION" }, "record": { "contributors": [ { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Eric Kolve", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Roozbeh Mottaghi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Daniel Gordon", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Yuke Zhu", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Abhinav Gupta", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Ali Farhadi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.", "domain": "Embodied-AI simulation infrastructure" }, "sources": [ { "citation": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1" } ], "statement": "# AI2-THOR: an interactive indoor simulation interface for visual AI\n\nAn image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform.\n\n## Scientific scope\n\nThe room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nEric Kolve; Roozbeh Mottaghi; Daniel Gordon; Yuke Zhu; Abhinav Gupta; Ali Farhadi\n\nSource-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.\n\n\n\n## Original sources\n\n- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://arxiv.org/abs/1712.05474v1)\n", "summary": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform.", "title": "AI2-THOR: an interactive indoor simulation interface for visual AI" }, "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "source_ref": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:4dfb4ad851e444babce9978efd3a2abdc8f2009fe595fe5149a346b9c03c79ae" }, "target": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "entry_id": "kolve-mottaghi-gordon-zhu-gupta-farhadi-ai2thor-2017", "revision": 1, "title": "AI2-THOR: an interactive indoor simulation interface for visual AI", "context": "Embodied-AI simulation infrastructure", "track": "Reusable scientific method, experimental result or historical framework", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-23", "evidence_cutoff": "2026-10-23", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform.", "scope": "An image can show a closed refrigerator, but it cannot reveal what changes when a robot opens it. AI2-THOR connects a simulated indoor world to a Python controller: an agent requests an action, the world applies its rules, and the agent receives a new image and structured information. The 2017 account describes 120 rooms across four household settings, with objects that can be opened, picked up or otherwise manipulated. This makes sequences of perception and action repeatable without requiring a physical robot for every experiment. Its contribution here is the documented simulation resource and interface, separate from the earlier image-target navigation algorithm developed with the platform. The room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.", "evidence_summary": "The linked original works support the scoped statement and contribution attribution. WRS has not independently replayed experiments or reconstructed a full mathematical proof. The room and object counts describe the cited 2017 version. Camera visibility, distance limits and object-action preconditions restrict what the agent can do; simulator metadata can expose information unavailable in an ordinary image. Reported execution speeds depend on hardware and settings. Simulation realism does not establish reliable transfer to arbitrary real homes. This resource record does not award a second copy of the navigation algorithm, Unity physics or subsequent AI2-THOR extensions.", "references": [ { "title": "AI2-THOR: An Interactive 3D Environment for Visual AI", "url": "https://arxiv.org/abs/1712.05474v1", "locator": "Original scoped contribution, statement/method and evidence limits; source-specific attribution retained." } ], "roles": [ { "who": "The named authors in the research record", "credit": "SCIENCE", "did": "Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred." } ], "target": { "record_id": "urn:uuid:aa08c180-a88c-4798-a344-597f05086ee2", "version": 1, "content_hash": "sha256:f507f7402630b5a068c150a55dc504a3d749459b5405a90a04a79d129f5312d1" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "150000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }