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About this paper
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Matt Deitke, Kiana Ehsani and 5 more
AI2-THOR provides an interactive simulator for embodied AI agents to navigate indoor scenes, manipulate objects, and receive updated visual observations. Researchers send actions through a Python interface to Unity, which returns camera images and environment information. Since 2017, it has been used in over 150 publications and downloaded over 500,000 times, demonstrating adoption, not proven transfer to real robots.
Key insights
- Active interaction within 3D environments is critical for AI to develop human-like visual understanding and causal reasoning, surpassing limitations of passive observation.
- High-fidelity interactive simulators offer a scalable, safe, and cost-effective alternative to real-world robot training, enabling extensive experimentation.
- Procedural generation of diverse indoor scenes is effective for creating vast training data, leading to improved generalization and robustness of learned policies to unseen environments.
Summary by alphaXiv.
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