# cuRobo *CUDA Accelerated Robot Library* **Check [curobo.org](https://curobo.org) for installing and getting started with examples!** Use [Discussions](https://github.com/NVlabs/curobo/discussions) for questions on using this package. Use [Issues](https://github.com/NVlabs/curobo/issues) if you find a bug. cuRobo's collision-free motion planner is available for commercial applications as a MoveIt plugin: [Isaac ROS cuMotion](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_cumotion) For business inquiries of this python library, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/) ## Overview cuRobo is a CUDA accelerated library containing a suite of robotics algorithms that run significantly faster than existing implementations leveraging parallel compute. cuRobo currently provides the following algorithms: (1) forward and inverse kinematics, (2) collision checking between robot and world, with the world represented as Cuboids, Meshes, and Depth images, (3) numerical optimization with gradient descent, L-BFGS, and MPPI, (4) geometric planning, (5) trajectory optimization, (6) motion generation that combines inverse kinematics, geometric planning, and trajectory optimization to generate global motions within 30ms.

cuRobo performs trajectory optimization across many seeds in parallel to find a solution. cuRobo's trajectory optimization penalizes jerk and accelerations, encouraging smoother and shorter trajectories. Below we compare cuRobo's motion generation on the left to a BiRRT planner for the motion planning phases in a pick and place task.

## Citation If you found this work useful, please cite the below report, ``` @misc{curobo_report23, title={cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation}, author={Balakumar Sundaralingam and Siva Kumar Sastry Hari and Adam Fishman and Caelan Garrett and Karl Van Wyk and Valts Blukis and Alexander Millane and Helen Oleynikova and Ankur Handa and Fabio Ramos and Nathan Ratliff and Dieter Fox}, year={2023}, eprint={2310.17274}, archivePrefix={arXiv}, primaryClass={cs.RO} } ```