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 high-fidelity 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.

翻译：我们提出了Orbit，一个基于NVIDIA Isaac Sim构建的统一且模块化的机器人学习框架。该框架采用模块化设计，能够轻松高效地创建具备照片级真实感场景和高保真刚体及可变形体仿真的机器人环境。借助Orbit，我们提供了一套涵盖不同难度的基准任务——从单阶段的柜门开启和布料折叠，到多阶段任务（如房间重组）。为支持多样化的观测与动作空间，我们集成了配备不同物理传感器和运动生成器的固定臂与移动式机械臂。通过利用基于GPU的并行化技术，Orbit能够在数分钟内完成强化学习策略的训练，并从手工设计或专家解决方案中收集大规模演示数据集。综上所述，我们提供了一个开源框架，该框架原生支持16种机器人平台、4种传感器模态、10种运动生成器、20余项基准任务，并提供了面向4个学习库的封装接口。基于这一框架，我们旨在支持表征学习、强化学习、模仿学习以及任务与运动规划等多个研究领域。我们期望该框架能促进这些领域的跨学科合作，其模块化特性也将使其易于扩展以适应未来更多的任务与应用场景。