ghoshaw / chalet

Cornell House Agent Learning Environment

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CHALET: Cornell House Agent Learning Environment

CHALET is a 3D house simulator with support for navigation and manipulation. Unlike existing systems, CHALET supports both a wide range of object manipulation, as well as supporting complex environemnt layouts consisting of multiple rooms. The range of object manipulations includes the ability to pick up and place objects, toggle the state of objects like taps or televesions, open or close containers, and insert or remove objects from these containers. In addition, the simulator comes with 58 rooms that can be combined to create houses, including 10 default house layouts. CHALET is therefore suitable for setting up challenging environments for various AI tasks that require complex language understanding and planning, such as navigation, manipulation, instruction following, and interactive question answering.

Code will be released soon. Send an email to Dipendra Misra (dkm@cs.cornell.edu) for more information.

Live Demo:

Available Soon

Project:

Developers: Claudia Yan, Dipendra Misra, Aaron Walsman and Yonatan Bisk.

Researchers: Dipendra Misra, Andrew Bennett, Aaron Walsman, Yonatan Bisk and Yoav Artzi

Publication:

  1. CHALET: Cornell House Agent Learning Environment, Claudia Yan, Dipendra Misra, Andrew Bennett, Aaron Walsman, Yonatan Bisk and Yoav Artzi, arXiv report 2018 [https://arxiv.org/abs/1801.07357]

Youtube Demos:

Demo 1 Demo 2 Demo 3

About

Cornell House Agent Learning Environment