Nvidia · AI Agent · GitHub · Hugging Face
Every Robot and every Simulation joins a Zenoh peer mesh automatically
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The agent calls robot_mesh(action="peers") to enumerate locals and discovered peers, then robot_mesh(action="broadcast",.) to send the structured command to every peer with a timeout.
Key facts
- LerobotLocalPolicy supports ACT, Diffusion Policy, SmolVLA, π0, and π0.5, anything LeRobot's own policy registry can resolve from a config.json
- 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
- To record demonstrations on a physical SO-101 instead of simulation, use LeRobot's record CLI directly
- For full SO-101 hardware setup, calibration walkthroughs, and troubleshooting, see the README in the example folder
Summary
You have a robot, a folder of demonstration data on the Hugging Face Hub, and a new task you want it to learn. 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 orchestrates. This post walks you through five steps inside a single agent: build the agent over the LeRobot AgentTools, record a demonstration as a LeRobotDataset in simulation, run a policy on the same robot, deploy the same agent code to a physical SO-101 with one keyword argument change, and broadcast commands across a fleet over the Zenoh mesh. No hardware, no GPU, no Hugging Face credentials needed for the default path.
Both modes share the same DatasetRecorder and the same policy providers, so a dataset captured in sim and a dataset captured on hardware use the same on-disk LeRobotDataset format.