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What you'll build Prerequisites Step 1 - Set up the example Step 2 - Record demonstrations and push to the Hub Step 3 - Run a policy in simulation Step 4 - Deploy the policy to physical hardware Step 5 - Coordinate multiple robots with the mesh Try it using the sample application Security Considerations Clean up How this fits together Where to go from here Resources Authors A walkthrough of the LeRobot integration in Strands Robots - one agent loop, from a Hub dataset to a physical robot, with sim-to-real datasets in the same on-disk format and policies you swap with a string.
You have a robot, a folder of demonstration data on the Hugging Face Hub, and a new task you want it to learn. Today that takes five separate tools: one to record new demonstrations, another to train, a third to test in simulation, custom code to deploy on hardware, and yet another to coordinate when you have more than one robot. The pieces work on their own. They don't talk to each other.
Strands Robots is an open source SDK from AWS (Apache 2.0) that exposes robot abstractions, simulation, and the LeRobot stack as AgentTools that you compose into a single Strands agent. The integration is deliberately thin: LeRobot's own scripts handle hardware recording and calibration, and the Strands AgentTools come in for the parts an agent actually orchestrates. The simulation tool records LeRobotDatasets in the same format LeRobot writes on hardware. GR00T and LerobotLocal serve policy inference behind a common interface, and MolmoAct2 checkpoints run through the LerobotLocal path. A peer mesh fans the agent out to remote robots. The dataset format stays exactly as LeRobot wrote it; the agent loop is the glue.
