Timezone: »

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
Chuang Gan · 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 · Aidan Curtis · Kevin Feigelis · Daniel Bear · Dan Gutfreund · David Cox · Antonio Torralba · James J DiCarlo · Josh Tenenbaum · Josh McDermott · Dan Yamins

Tue Dec 07 01:25 AM -- 01:35 AM (PST) @ None

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.

#### Author Information

##### Damian Mrowca (Stanford University)

Young children are excellent at playing, an ability to explore and (re)structure their environment that allows them to develop a remarkable visual and physical representation of their world that sets them apart from even the most advanced robots. Damian Mrowca is studying (1) representations and architectures that allow machines to efficiently develop an intuitive physical understanding of their world and (2) mechanisms that allow agents to learn such representations in a self-supervised way. Damian is a 3rd year PhD student co-advised by Prof. Fei-Fei Li and Prof. Daniel Yamins. He received his BSc (2012) and MSc (2015) in Electrical Engineering and Information Theory, both from the Technical University of Munich. During 2014-2015 he was a visiting student with Prof. Trevor Darrell at UC Berkeley. After a year in start-up land, looking to apply his research in businesses, he joined the Stanford Vision Lab and NeuroAILab in September 2016.

##### James J DiCarlo (Massachusetts Institute of Technology)

Prof. DiCarlo received his Ph.D. in biomedical engineering and his M.D. from Johns Hopkins in 1998, and did his postdoctoral training in primate visual neurophysiology at Baylor College of Medicine. He joined the MIT faculty in 2002. He is a Sloan Fellow, a Pew Scholar, and a McKnight Scholar. His lab’s research goal is a computational understanding of the brain mechanisms that underlie object recognition. They use large-scale neurophysiology, brain imaging, optogenetic methods, and high-throughput computational simulations to understand how the primate ventral visual stream is able to untangle object identity from other latent image variables such as object position, scale, and pose. They have shown that populations of neurons at the highest cortical visual processing stage (IT) rapidly convey explicit representations of object identity, and that this ability is reshaped by natural visual experience. They have also shown how visual recognition tests can be used to discover new, high-performing bio-inspired algorithms. This understanding may inspire new machine vision systems, new neural prosthetics, and a foundation for understanding how high-level visual representation is altered in conditions such as agnosia, autism and dyslexia.

##### Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).