AI2-THOR
E366086
AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
All labels observed (1)
| Label | Occurrences |
|---|---|
| AI2-THOR canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3520098 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AI2-THOR Context triple: [Allen Institute for Artificial Intelligence, knownFor, AI2-THOR]
-
A.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
B.
PettingZoo
PettingZoo is a Python library that provides a standardized interface and tools for developing, running, and benchmarking multi-agent reinforcement learning environments.
-
C.
Codey Arena
Codey Arena is a public ice skating and hockey facility in West Orange, New Jersey, known for hosting local teams, events, and recreational skating.
-
D.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
E.
Simulator
Simulator is Apple's iOS and watchOS device emulation tool bundled with Xcode, used by developers to run and test apps on virtual Apple devices.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AI2-THOR Target entity description: AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
-
A.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
B.
PettingZoo
PettingZoo is a Python library that provides a standardized interface and tools for developing, running, and benchmarking multi-agent reinforcement learning environments.
-
C.
Codey Arena
Codey Arena is a public ice skating and hockey facility in West Orange, New Jersey, known for hosting local teams, events, and recreational skating.
-
D.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
E.
Simulator
Simulator is Apple's iOS and watchOS device emulation tool bundled with Xcode, used by developers to run and test apps on virtual Apple devices.
- F. None of above. chosen
Statements (66)
| Predicate | Object |
|---|---|
| instanceOf |
3D simulation environment
ⓘ
AI research tool ⓘ embodied AI benchmark ⓘ simulation platform ⓘ |
| affiliation | Allen Institute for Artificial Intelligence projects ⓘ |
| developer |
Allen Institute for Artificial Intelligence
ⓘ
surface form:
Allen Institute for AI
|
| goal |
bridge simulation and real-world robotics
ⓘ
enable reproducible embodied AI experiments ⓘ |
| hasDimension | 3D ⓘ |
| hasEnvironmentType |
bathrooms
ⓘ
bedrooms ⓘ household environments ⓘ indoor scenes ⓘ kitchens ⓘ living rooms ⓘ offices ⓘ |
| hasFeature |
continuous control support
ⓘ
depth observations ⓘ discrete action space ⓘ domain randomization options ⓘ evaluation metrics ⓘ high-level actions API ⓘ interactive objects ⓘ low-level control API ⓘ metadata for objects ⓘ multi-room scenes ⓘ object affordances ⓘ physics-enabled environment ⓘ procedural scene variations ⓘ segmentation masks ⓘ task definitions ⓘ visual observations ⓘ |
| hasInterface |
Python API
ⓘ
Unity environment build ⓘ |
| license | open-source ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
RGB images
ⓘ
agent pose information ⓘ depth maps ⓘ instance segmentation ⓘ object bounding boxes ⓘ object states ⓘ semantic segmentation ⓘ |
| renderingEngine |
Unity game engine
ⓘ
surface form:
Unity
|
| shortName |
Thor
ⓘ
surface form:
THOR
|
| supports |
computer vision research
ⓘ
embodied AI agents ⓘ imitation learning experiments ⓘ interactive manipulation tasks ⓘ multi-scene training ⓘ multi-task learning ⓘ reinforcement learning experiments ⓘ visual navigation research ⓘ |
| supportsIntegrationWith |
OpenAI Gym-style interfaces
ⓘ
reinforcement learning libraries ⓘ |
| supportsPlatform |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| usedFor |
evaluating embodied agents
ⓘ
navigation tasks ⓘ object manipulation tasks ⓘ object search tasks ⓘ robotics simulation ⓘ task planning research ⓘ training embodied agents ⓘ vision-and-language tasks ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: AI2-THOR Description of subject: AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.