cuRobo and cuMotion#

Note

There are known issues with using NvBlox examples within cuRobo. This tutorial will be updated when cuRobo is updated to resolve these issues.

Learning Objectives#

cuRobo (also on GitHub) is a high-performance, GPU-accelerated robotics motion generation library for robot manipulators, developed by NVIDIA Research. It is a standalone Python library that interfaces directly with NVIDIA Isaac Sim, simplifying both testing in simulation and deploying on physical robots.

NVIDIA cuMotion, available as a Developer Preview in Isaac 3.0, is a production motion generation package for manipulators. The current version leverages cuRobo as its backend, providing collision-free motion planning using a plugin for MoveIt 2 and a set of supporting ROS 2 packages. For an example of using cuMotion with NVIDIA Isaac Sim using the ROS 2 bridge, see the relevant section of the Isaac ROS documentation. This example is somewhat limited in Isaac 3.0 but will be expanded in a future release.

In the remainder of this tutorial, we focus on direct integration of cuRobo into NVIDIA Isaac Sim, covering cuRobo installation and use, with examples for collision-free inverse kinematics, motion planning, and reactive control (MPPI).

../_images/isaac_tutorial_advanced_cuRobo.gif

Getting Started#

Prerequisites

Complete the Adding a Manipulator Robot tutorial prior to beginning this tutorial.

Installation#

Follow the cuRobo installation instructions for installing cuRobo and required libraries. cuRobo supports NVIDIA Isaac Sim 2022.2.1 and later. Follow the workstation installation instructions to install NVIDIA Isaac Sim.

Examples#

Using Isaac Sim with cuRobo#

In the cuRobo documentation, refer to the “Using Isaac Sim” section for an overview of how cuRobo is interfaced to Isaac Sim, along with a series of standalone examples demonstrating collision checking, motion generation, inverse kinematics, model-predictive control, and multi-arm reaching.

Using Isaac Sim with cuRobo and nvblox#

In the cuRobo documentation, refer to the “Using with Depth Camera” section for examples of obstacle-aware motion generation in NVIDIA Isaac Sim, both with pre-generated signed distance fields (SDFs) from nvblox and with online mapping leveraging nvblox with a physical RealSense depth camera.