cuRoboV2-Fast plans collision-free robot motion in 4.4 ms

Balakumar Sundaralingam@balakumar_

In 2022, our first cuRobo prototype took 100 ms and consumed the whole GPU.

Today, we’re open-sourcing cuRoboV2-Fast under Apache 2.0: collision-free, torque-limited motion planning in 4.4 ms.

Collaborating with researchers across robotics and GPU architecture brought us to 50 ms, then 30 ms. B-splines, GPU time optimization, and faster kernels brought us to 4.4 ms while producing motions that execute faster.

As planning got faster, perception, grasping, and control became the bottlenecks. We built a 7 ms perception pipeline (SceneFields): from stereo RGB to depth, voxels, and robot and object segmentation, and accelerated GraspGenX to 1.6 ms. We also integrated wrote a cpp controller for YAM and built a low-latency CUDA-GL visualizer for an interactive live view of the task.

We deploy the stack as a two-process application: one dedicated to real-time control, the other handling perception, grasping, planning, and visualization.

On a real YAM arm, pick-and-place cycles averaged 6 seconds, 34.6% shorter than cuRoboV2.

A long journey, made possible by amazing collaborators. The rest of the stack is coming soon.

t.co/IDCfrxtN0l
Joint work with @Adithya_Murali_ , @BirchfieldStan

Prompt your agent with :
Use cuRobo cpp_runtime for collision-free motion planning: