← AI GitHub

AI GitHub

RoboCasa for simulated household manipulation

Study manipulation tasks in simulated kitchens with varied scenes and objects.

Science and ResearchPublished 2 min read

Key Takeaways

  • RoboCasa supplies household task environments and demonstrations for policy learning.
  • Pick the task and supported simulator configuration before selecting demonstrations.
  • Train or evaluate on a small task and reserve several kitchens for testing.
  • Scene variation increases difficulty and data needs.

How it works

RoboCasa supplies household task environments and demonstrations for policy learning. Variation across layouts and objects lets an experiment test whether behavior transfers beyond one scene.

What you need

Pick the task and supported simulator configuration before selecting demonstrations. Record the train and evaluation layouts so the split is reproducible.

What to try first

Train or evaluate on a small task and reserve several kitchens for testing. Inspect failure recordings to see whether errors come from navigation, grasping or sequencing.

Evaluate on a new arrangement with the same high-level goal. A recording of the attempt helps reveal whether the policy learned the task or memorized one kitchen layout.

Where to look for failures

Scene variation increases difficulty and data needs. Keep train and evaluation scenes separate and record the simulator and asset versions used.

Related Franklin pages

Start with saved data or a simulator before deploying a policy on hardware. Verify observation layout, action conventions and task success using a known reference episode.

Franklin’s robotics coverage gives examples of where simulation, training and hardware meet. Franklin’s robotics coverage

Project documentation

Try it for your workflow

Explore the project’s documentation for setup and usage, or save this page to a collection for later.

Read the project documentation ↗