Files
lerobot/docs/source/earthrover_mini_plus.mdx
Caroline Pascal 3dd19d043e feat(depth maps): adding support for depth in LeRobot (#3644)
* feat(depth): add depth quantization helpers and tests

* feat(video): add ffv1 to supported codecs

* feat(depth): persist depth metadata

* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline

* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter

* feat(depth): wire StreamingVideoEncoder + writer to depth encoder

* feat(depth): wire DatasetReader to decode_depth_frames

* feat(cameras/realsense): expose async depth in metric meters

* feat(features): route 2D camera shapes to observation.depth.<key>

* feat(robots/so_follower): emit + populate depth keys when use_depth

* feat(record): plumb DepthEncoderConfig through lerobot-record

* feat(viz): render depth observations as rr.DepthImage in Viridis

* feat(depth maps writer): adding support for raw depth maps recording with image writer

* chore(format): format code

* feat(depth shape): ensuring depth maps shape is always including the channel

* feat(is_depth): simplifying is_depth nested name + legacy support

* fix(stop_event): fixing stop_event race condition in camera classes

* fix(plumbing): fixing missing parts in the depth maps pipeline

* chore(typos): fixing typos

* test(fix): fixing exisiting tests to still work with latest features

* tests(depth): adding new tests for depth integration validation

* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels

* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.

* fix(pre-commit): fixing mutable defautl value

* fix(info): fixing info metadata update when is_depth_map was set

* tests(typos): fixing typos in tests

* fix(realsense): fixing typo in realsense serial number

* fix(normalization): restricting 255 normalization to non depth/uint8 images only

* fix(typo): fixing typo

* fix(TIFF): add missing quantization and cleanup for TIFF files

* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization

* feat(tools): adding depth support in LeRobotDataset edition tools

* test(aggregate): extending aggregation tests to depth frames

* test(cleaning): cleaning up tests

* fix(from_video_info): fixing early validation issue in from_video_info

* fix(typo): fixing typo

* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* fix(depth units): fixing depth units output for the realsense cameras

* feat(output unit): adding support for output unit specification at dataset reading/training time

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* test(depth): cleaning up depth tests

* test(depth encoding): updating and cleaning video/depth encoding tests

* chore(format): formatting code

* docs(depth): improving depth maps docs

* test(fix): fixing depth tests

* test(dataset tools): adding missing tests for new dataset edition tools features

* chore(format): formatting code

* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

* docs(mermaid): fixing mermaid diagram

* fix(rebase): rebase follow up corrections

* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools

* docs(docstring): updating docstrings

* docs(dataset tools): updating docs

* fix(save images): fixing image saving in dataset tools

* fix(update video info): fixing update video info logic to match the recording and editing use cases

* test(reencode): fixing reencoding monkeypatch

* fix(review): add Claude review

* chore(format): format code

* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.

* chore(rebase): fixing rebase merge conflicts

* test(visualization): fixing visualization tests

* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.

* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit

* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity

* chore(TODO): removing deprecated TODO

* doc(write_u16_plane): improving docstrings for write_u16_plane

* feat(units): adding constants for depth frames units (m and mm)

* fix(spam): replacing spamming warning but a debug log

* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature

* fix(copy&reindex): fixing metadat reshaping for single channel frames

* fix(ImageNet): excluding dpeth frames from ImageNet stats

* fix(PyAV container seek): fixing initial  PyAV container seek to be robust againsy codec choice

* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz

* fix(compress): removing rerun compression for DepthImages

* fix(signle channel squeeze): fixing single channel squeezing

* chore(format): format code

* fix(streaming): adding support for dequantization in streaming_dataset.py

* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage

* chore(renaming): fixing missed RGBEncoderConfig renamings

* docs(renaming): reflecting renamings in a clearer way in the docs

* chore(annotation): excluding depth from the annotation pipeline

* feat(robots): adding depth support in compatible follower robots

* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)

* chore(fail): removing misplaced file

* chore(fail): removing misplaced file

* fix(remove ffv1): removing ffv1 as it does not support MP4

* docs(cheat sheet): adding depth and video encoding to the cheat sheet

* fix(lossless): tuning depth encoding parameters for lossless depth storage

* test(fix): fixing failing tests

* depth(ZMQ): excluding ZMQ from depth support

* Revert "depth(ZMQ): excluding ZMQ from depth support"

This reverts commit b95cf4e4c2.

* fix(image transforms): excluding depth frames from images transforms

* fix(typo): typo

* fix(stats): fixing stats computation for depth frames

* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images

* fix(typos): fixing typos

* test(dtype): fixing stats computation typing tests

---------

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>
2026-06-27 14:21:21 +02:00

239 lines
6.3 KiB
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# EarthRover Mini Plus
<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Earth_Rover_Mini_5_240c9adc-4f9e-44b7-982f-5d1dc24af1d8.png.webp"
alt="EarthRover Mini Plus"
width="70%"
/>
The EarthRover Mini Plus is a fully open source mobile robot that connects through the cloud using the Frodobots SDK. This lets you control the robot and record datasets for training AI models.
## What You Need
### Hardware
- EarthRover Mini robot
- Computer with Python 3.12 or newer
- Internet connection
### Setting Up the Frodobots SDK
The robot needs the [Frodobots SDK](https://github.com/frodobots-org/earth-rovers-sdk) running on your computer. Here's how:
1. Download and install the SDK:
```bash
git clone https://github.com/frodobots-org/earth-rovers-sdk.git
cd earth-rovers-sdk
pip install -r requirements.txt
```
2. Save Credentials:
Write your .env variables with the SDK API key and bot name provided by the Frodobots team.
```bash
SDK_API_TOKEN=your_sdk_api_token_here
BOT_SLUG=your_bot_slug_here
CHROME_EXECUTABLE_PATH=/path/to/chrome_or_chromium
# Default value is MAP_ZOOM_LEVEL=18 https://wiki.openstreetmap.org/wiki/Zoom_levels
MAP_ZOOM_LEVEL=18
MISSION_SLUG=your_mission_slug_here
# Image quality between 0.1 and 1.0 (default: 0.8)
# Recommended: 0.8 for better performance
IMAGE_QUALITY=0.8
# Image format: jpeg, png or webp (default: png)
# Recommended: jpeg for better performance and lower bandwidth usage
IMAGE_FORMAT=jpeg
```
3. Start the SDK:
```bash
hypercorn main:app --reload
```
4. Open your web browser and go to `http://localhost:8000`, then click "Join"
The SDK gives you:
- Live video from front and rear cameras
> [!IMPORTANT]
> The SDK must be running before you can use the robot.
## Install LeRobot
Follow our [Installation Guide](./installation) to install LeRobot.
In addition to the base installation, install the EarthRover Mini with hardware dependencies:
```bash
pip install -e ".[hardware]"
```
## How It Works
The robot uses the internet to communicate:
- **Movement commands**: Sent through the SDK
- **Camera video**: Received from the SDK
- **Robot info**: Battery, location, speed from the SDK
You don't need to plug anything in - it all works through the SDK.
## Calibration
No calibration needed! The robot is ready to use as soon as the SDK is running.
## Controlling the Robot
You control the robot using your keyboard - just like playing a video game with WASD keys.
### Keyboard Controls
| Key | Action |
| --- | -------------------------------- |
| W | Move forward |
| S | Move backward |
| A | Turn left (with forward motion) |
| D | Turn right (with forward motion) |
| Q | Rotate left in place |
| E | Rotate right in place |
| X | Stop all movement |
| +/= | Increase speed |
| - | Decrease speed |
| ESC | Disconnect |
### Speed Settings
You can adjust how fast the robot moves:
- **Forward/backward speed**: Default is full speed (1.0)
- **Turning speed**: Default is full speed (1.0)
- **Speed changes**: Use +/- keys to adjust by 0.1 each time
### Try It Out
Test driving the robot before recording data:
```python
from lerobot.robots.earthrover_mini_plus import EarthRoverMiniPlus, EarthRoverMiniPlusConfig
from lerobot.teleoperators.keyboard import KeyboardRoverTeleop, KeyboardRoverTeleopConfig
# Initialize robot
robot_config = EarthRoverMiniPlusConfig()
robot = EarthRoverMiniPlus(robot_config)
# Initialize teleoperator
teleop_config = KeyboardRoverTeleopConfig(
linear_speed=1.0,
angular_speed=1.0,
speed_increment=0.1
)
teleop = KeyboardRoverTeleop(teleop_config)
# Connect
robot.connect()
teleop.connect()
# Teleoperate (use keyboard controls)
try:
while True:
action = teleop.get_action()
robot.send_action(action)
except KeyboardInterrupt:
pass
finally:
robot.disconnect()
teleop.disconnect()
```
> [!TIP]
> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
## Recording Data
Once you can drive the robot well, you can start recording data to train AI models. The system records:
- **What you do**: How you move the robot (forward, backward, turning)
- **What the robot sees**:
- Videos from both cameras
- Robot speed and direction
- Battery level and location
- GPS position and signal
- Other sensor data
- **When it happened**: Timestamps for everything
### Setting Up Hugging Face
We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):
```bash
hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
```
Store your Hugging Face username:
```bash
HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
echo $HF_USER
```
### Start Recording
Use the standard recording command:
```bash
lerobot-record \
--robot.type=earthrover_mini_plus \
--teleop.type=keyboard_rover \
--dataset.repo_id=your_username/dataset_name \
--dataset.num_episodes=2 \
--dataset.fps=10 \
--dataset.single_task="Navigate around obstacles" \
--dataset.streaming_encoding=true \
--dataset.encoder_threads=2 \
# --dataset.rgb_encoder.vcodec=auto \
--display_data=true
```
Replace `your_username/dataset_name` with your Hugging Face username and a name for your dataset.
### What Gets Saved
Your dataset includes:
**Your Actions (2 features)**:
- `linear_velocity`: How much you moved forward/backward
- `angular_velocity`: How much you turned left/right
**Robot Observations (24 features)**:
- Front camera video
- Rear camera video
- Current speed
- Battery level
- Orientation
- GPS (latitude, longitude, signal strength)
- Network signal strength
- Vibration level
- Lamp state (on/off)
- Accelerometer (x, y, z)
- Gyroscope (x, y, z)
- Magnetometer (x, y, z)
- Wheel RPMs (4 wheels)
### Where Your Data Goes
On your computer: `~/.cache/huggingface/lerobot/{repo-id}`
After recording, your data automatically uploads to your Hugging Face page:
```bash
echo https://huggingface.co/datasets/${HF_USER}/earthrover-navigation
```
Your dataset will be tagged with `LeRobot` for community discovery.