What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Eric Kolve; Roozbeh Mottaghi; Daniel Gordon; Yuke Zhu; Abhinav Gupta; Ali Farhadi
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.
Research locations and affiliations
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English catalogue narrative
AI2-THOR: an interactive indoor simulation interface for visual AI
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.
Scientific scope
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.
Reading status
This 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.
People and systems
Eric Kolve; Roozbeh Mottaghi; Daniel Gordon; Yuke Zhu; Abhinav Gupta; Ali Farhadi
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.