diff --git a/README.md b/README.md
index b7c3af23..7d7b80d4 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,7 @@ conda activate lerobot
Install 🤗 LeRobot:
```bash
-pip install .
+pip install -e .
```
> **NOTE:** Depending on your platform, If you encounter any build errors during this step
@@ -91,7 +91,7 @@ For simulations, 🤗 LeRobot comes with gymnasium environments that can be inst
For instance, to install 🤗 LeRobot with aloha and pusht, use:
```bash
-pip install ".[aloha, pusht]"
+pip install -e ".[aloha, pusht]"
```
To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
@@ -116,10 +116,12 @@ wandb login
| | ├── datasets # various datasets of human demonstrations: aloha, pusht, xarm
| | ├── envs # various sim environments: aloha, pusht, xarm
| | ├── policies # various policies: act, diffusion, tdmpc
+| | ├── robot_devices # various real devices: dynamixel motors, opencv cameras, koch robots
| | └── utils # various utilities
| └── scripts # contains functions to execute via command line
| ├── eval.py # load policy and evaluate it on an environment
| ├── train.py # train a policy via imitation learning and/or reinforcement learning
+| ├── control_robot.py # teleoperate a real robot, record data, run a policy
| ├── push_dataset_to_hub.py # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub
| └── visualize_dataset.py # load a dataset and render its demonstrations
├── outputs # contains results of scripts execution: logs, videos, model checkpoints
diff --git a/docker/lerobot-cpu/Dockerfile b/docker/lerobot-cpu/Dockerfile
index 885a7752..7ad3bf6e 100644
--- a/docker/lerobot-cpu/Dockerfile
+++ b/docker/lerobot-cpu/Dockerfile
@@ -9,6 +9,7 @@ ARG DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential cmake \
libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+ speech-dispatcher \
&& apt-get clean && rm -rf /var/lib/apt/lists/*
# Create virtual environment
diff --git a/docker/lerobot-gpu-dev/Dockerfile b/docker/lerobot-gpu-dev/Dockerfile
index f2c06fd3..1f525576 100644
--- a/docker/lerobot-gpu-dev/Dockerfile
+++ b/docker/lerobot-gpu-dev/Dockerfile
@@ -13,6 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
sed gawk grep curl wget zip unzip \
tcpdump sysstat screen tmux \
libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
+ speech-dispatcher \
python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
&& apt-get clean && rm -rf /var/lib/apt/lists/*
diff --git a/docker/lerobot-gpu/Dockerfile b/docker/lerobot-gpu/Dockerfile
index 7a27e8f0..4d6f772b 100644
--- a/docker/lerobot-gpu/Dockerfile
+++ b/docker/lerobot-gpu/Dockerfile
@@ -9,6 +9,7 @@ ARG DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential cmake \
libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+ speech-dispatcher \
python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& apt-get clean && rm -rf /var/lib/apt/lists/*
diff --git a/examples/7_get_started_with_real_robot.md b/examples/7_get_started_with_real_robot.md
new file mode 100644
index 00000000..482587c7
--- /dev/null
+++ b/examples/7_get_started_with_real_robot.md
@@ -0,0 +1,1023 @@
+# Getting Started with Real-World Robots
+
+This tutorial will guide you through the process of setting up and training a neural network to autonomously control a real robot.
+
+**What You'll Learn:**
+1. How to order and assemble your robot.
+2. How to connect, configure, and calibrate your robot.
+3. How to record and visualize your dataset.
+4. How to train a policy using your data and prepare it for evaluation.
+5. How to evaluate your policy and visualize the results.
+
+By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
+
+Although this tutorial is general and can be easily adapted to various types of robots by changing the configuration, it is specifically based on the [Koch v1.1](https://github.com/jess-moss/koch-v1-1), an affordable robot. The Koch v1.1 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
+
+During the data collection phase, you'll control the follower arm by moving the leader arm, a process known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
+
+If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb).
+
+## 1. Order and Assemble your Koch v1.1
+
+Follow the sourcing and assembling instructions provided on the [Koch v1.1 Github page](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
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1. Zero position
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2. Rotated position
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3. Rest position
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1. Zero position
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2. Rotated position
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3. Rest position
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