There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …

Physics-based simulation provides an accelerated and safe avenue for developing,
verifying, and testing robotic control algorithms and prototype designs. In the quest to …

robosuite is a simulation framework for robot learning powered by the MuJoCo physics
engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark …

Building home assistant robots has long been a goal for vision and robotics researchers. To
achieve this task, a simulated environment with physically realistic simulation, sufficient …

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
TDW enables simulation of high-fidelity sensory data and physical interactions between …

We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal
representation of a dynamic scene from a set of RGB images. Key to our approach is the use …