  We present ORBIT, a unified and modular framework for robot learning powered
by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently
create robotic environments with photo-realistic scenes and fast and accurate
rigid and deformable body simulation. With ORBIT, we provide a suite of
benchmark tasks of varying difficulty -- from single-stage cabinet opening and
cloth folding to multi-stage tasks such as room reorganization. To support
working with diverse observations and action spaces, we include fixed-arm and
mobile manipulators with different physically-based sensors and motion
generators. ORBIT allows training reinforcement learning policies and
collecting large demonstration datasets from hand-crafted or expert solutions
in a matter of minutes by leveraging GPU-based parallelization. In summary, we
offer an open-sourced framework that readily comes with 16 robotic platforms, 4
sensor modalities, 10 motion generators, more than 20 benchmark tasks, and
wrappers to 4 learning libraries. With this framework, we aim to support
various research areas, including representation learning, reinforcement
learning, imitation learning, and task and motion planning. We hope it helps
establish interdisciplinary collaborations in these communities, and its
modularity makes it easily extensible for more tasks and applications in the
future. For videos, documentation, and code: https://isaac-orbit.github.io/.

Robotics;Artificial Intelligence