ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable ``avatars” that embody AI agents; and support for human interactions with VR devices. TDW’s API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that ‘learn like a child’, and attention studies in humans and neural networks.

Authors

• Chuang Gan University of Massachusetts at Amherst
• Jeremy Schwartz
• Seth Alter
• Damian Mrowca
• Martin Schrimpf
• James Traer
• Julian De Freitas
• Jonas Kubilius
• Abhishek Bhandwaldar
• Nick Haber
• Megumi Sano
• Kuno Kim
• Elias Wang
• Michael Lingelbach Stanford University
• Aidan Curtis
• Kevin Feigelis
• Daniel Bear
• Dan Gutfreund MIT-IBM Watson AI Lab
• David Cox
• Antonio Torralba
• James J DiCarlo
• Josh Tenenbaum
• Josh McDermott
• Dan Yamins

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