rerun

Visualize robot and machine-learning data with Rerun.

SKILL.md

GitHub

Rerun

Choose the data sink first. Where and when the viewer must run determines how the application should log.

Choose the viewing shape

  • Use a spawned viewer for short local exploration with a real desktop.
  • Stream to a separate viewer or serve one when the producer is remote or headless.
  • Save an .rrd when the run must be inspectable later, reproducible in CI, or shared without keeping the producer alive.
  • Embed a viewer only when it is part of the actual application experience; otherwise keep visualization out of the service boundary.

Make the recording understandable

  • Organize entity paths around the robot, sensors, policy, and world so related data shares a useful hierarchy.
  • Use an explicit sequence or time timeline for episodes and rollouts. Log static geometry once and changing state at the corresponding step.
  • Align transforms, images, actions, predictions, and outcomes on the same time basis before interpreting what the viewer shows.
  • Check the current Rerun documentation and examples before choosing archetypes or calling APIs. Names and signatures change between releases.

Go deeper only when needed

  • For local, remote, file, and browser modes plus their common failures, read OPERATING-MODES.md.
  • For the Robium-tested Gradio streaming pattern and dependency conflict, read GRADIO.md.
  • Let LeRobot's dataset visualizer produce the recording when inspecting a LeRobot episode; use LeRobot guidance for its current command.
  • When the boundary is live ROS topics, use RViz2 or Foxglove. Use the visualization skill only when the tool itself has not been chosen.

Done

  • The chosen sink works in the target environment, related data is aligned and navigable, and a saved or replayable artifact exists when the observation matters beyond the live session.