2026-09-19T10:36:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1514112023-08-01T03:55:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Torralba, Antonio
Ren, Jordan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:37:35Z
2023-07-31T19:37:35Z
2023-06
2023-06-06T16:35:20.512Z
https://hdl.handle.net/1721.1/151411
Embodied environments act as a tool that enables various control tasks to be learned. Within these simulators, having realistic rendering and physics ensures that the sim2real gap for tasks isn’t too large. Current embodied environments focus mainly on small-scale or low-level tasks, without the capability to learn large-scale diverse tasks, and often lack the realism for a small sim2real gap. To address the shortcomings of current simulators, we propose VirtualCity, a large-scale embodied environment that enables the learning of high-level planning tasks with photo-realistic rendering and realistic physics. To interact with VirtualCity, we provide a user-friendly Python API that allows the modification, control, and observation of the environment and its agents within. Building this realistic environment brings us closer to adapting models trained in simulation to solve real-world tasks.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Simulating Real-World Human Activities with
VirtualCity: A Large-Scale Embodied Environment
for 2D, 3D, and Language-Driven Tasks
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Embodied environments act as a tool that enables various control tasks to be learned. Within these simulators, having realistic rendering and physics ensures that the sim2real gap for tasks isn’t too large. Current embodied environments focus mainly on small-scale or low-level tasks, without the capability to learn large-scale diverse tasks, and often lack the realism for a small sim2real gap. To address the shortcomings of current simulators, we propose VirtualCity, a large-scale embodied environment that enables the learning of high-level planning tasks with photo-realistic rendering and realistic physics. To interact with VirtualCity, we provide a user-friendly Python API that allows the modification, control, and observation of the environment and its agents within. Building this realistic environment brings us closer to adapting models trained in simulation to solve real-world tasks.</Abstract>
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