diff --git a/docs/source/index.mdx b/docs/source/index.mdx
index e29bb185b..c8fec33b8 100644
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@@ -11,7 +11,7 @@
**State-of-the-art machine learning for real-world robotics**
-🤗 LeRobot provides a hardware-agnostic, Python-native interface for controlling real robots - from affordable arms like the SO-101 to full humanoids. Plus the tools to record, store, and share the datasets they generate. Every dataset uses the standardized **LeRobotDataset** format (synchronized video + action/state data) and can be streamed directly from the [Hugging Face Hub](https://huggingface.co/lerobot).
+🤗 LeRobot provides a hardware-agnostic, Python-native interface for controlling real robots - from affordable arms like the SO-ARM101 to full humanoids. Plus the tools to record, store, and share the datasets they generate. Every dataset uses the standardized **LeRobotDataset** format (synchronized video + action/state data) and can be streamed directly from the [Hugging Face Hub](https://huggingface.co/lerobot).
🤗 On top of that data, LeRobot implements state-of-the-art policies - from lightweight imitation-learning models like ACT to large vision-language-action models like π₀ and SmolVLA - all trainable, shareable, and deployable with the same handful of CLI commands.
@@ -33,6 +33,15 @@ The goal: lower the barrier to entry for robotics, so that everyone can contribu
+## How It Works
+
+**Teleoperate → Record → Train → Deploy**
+
+1. **Teleoperate** - control the robot yourself (with a leader arm, keyboard, or phone) so it can learn from your movements.
+2. **Record** - each demonstration is saved as a dataset: synchronized camera video plus the actions you took.
+3. **Train** - a policy (the neural network that will control the robot) learns to imitate your demonstrations.
+4. **Deploy** - run the trained policy on the robot and watch it complete the task on its own.
+
## Get Started
New here? [Install LeRobot](./installation), then pick your path:
@@ -59,7 +68,9 @@ New here? [Install LeRobot](./installation), then pick your path:
Once it's assembled and calibrated, record a dataset and train your first - policy with the imitation learning tutorial. + policy with the imitation learning tutorial - + or skip the CLI entirely with LeLab, a browser GUI + for the same workflow.