From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot

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. LerobotLocal serves policy inference behind one interface for GR00T , ACT, SmolVLA, and MolmoAct2 checkpoints. 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.

The Mock policy is intentional: it generates placeholder joint actions so the workflow runs end-to-end without a trained checkpoint. The robot moves through random motions rather than completing the grasp, and the recording is structurally complete (valid joint states, valid camera frames, a well-formed LeRobotDataset episode), but the demonstration itself isn't useful as training data. Step 3 below swaps in a GR00T checkpoint through LerobotLocal for real grasping behavior. To see actual cube-picking in this step, run --policy lerobot_local --checkpoint allenai/MolmoAct2-SO100_101 (a MolmoAct2 checkpoint , auto-detected from its config.json and routed through the LerobotLocal path); the prompt, dataset format, and agent code stay the same.

The dataset that lands on the Hub from this command is in the same format as the simulation recording. To fine-tune a policy on it, run LeRobot's training CLI ( lerobot-train ); training itself is out of scope for this post and follows the standard LeRobot workflow. From Step 3 onward, the agent picks up either the original or a fine-tuned checkpoint interchangeably. For full SO-101 hardware setup, calibration walkthroughs, and troubleshooting, see the README in the example folder .

The Strands Robots SDK exposes the LeRobot stack as AgentTools that you compose into one Strands agent. The example agent in this post does four things: record new demonstrations in simulation, push the result to the Hub as a LeRobotDataset, run a policy in simulation against that same format, and deploy the same agent code to a physical robot with one keyword argument change. When you have more than one robot, the agent can coordinate the whole fleet through a built-in peer mesh. For hardware recording and calibration, LeRobot's own CLIs ( lerobot-record , lerobot-calibrate ) handle the bring-up; the agent picks up from there.

Figure 1. Robot("so100") defaults to a MuJoCo-backed simulation; mode="real" returns a hardware robot driven by LeRobot. 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.

Two design choices make this work. First, Robot("so100") returns a simulation by default (no hardware, no risk), and mode="real" returns a hardware-backed robot driven by LeRobot. The agent code is identical across both modes. Second, the DatasetRecorder that writes a LeRobotDataset is shared between the simulation path and LeRobot's own hardware recording, so a dataset captured in MuJoCo and one captured from a physical SO-101 are in the same format.

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. At the end, you can clone the working sample application from GitHub and run it on your laptop in simulation. No hardware, no GPU, no Hugging Face credentials needed for the default path. The runnable companion to this post lives at examples/lerobot/hub_to_hardware.py and hub_to_hardware.ipynb . The notebook is sim-only and Mock-policy by default.

That's it. The example in this post runs end-to-end on a laptop with these three.

Export your Hugging Face token if you want the agent to push datasets or pull policies from the Hub. This is optional for the default simulation path in this post; the example runs end-to-end with the Mock policy and writes the dataset to your local cache without needing Hub access.