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Merge remote-tracking branch 'origin/main' into user/aliberts/2025_02_25_refactor_robots

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This commit is contained in:
Simon Alibert
2025-03-13 14:24:50 +01:00
parent d6f1359e69 974028bd28
commit ce63cfdb25
88 changed files with 151 additions and 154 deletions
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10
lerobot/common/cameras/intel/camera_realsense.py
View File

@@ -49,7 +49,7 @@ def find_cameras(raise_when_empty=True, mock=False) -> list[dict]:
connected to the computer.
"""
if mock:
import tests.mock_pyrealsense2 as rs
import tests.cameras.mock_pyrealsense2 as rs
else:
import pyrealsense2 as rs
@@ -101,7 +101,7 @@ def save_images_from_cameras(
serial_numbers = [cam["serial_number"] for cam in camera_infos]
if mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2
@@ -252,7 +252,7 @@ class RealSenseCamera(Camera):
self.logs = {}
if self.mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2
@@ -284,7 +284,7 @@ class RealSenseCamera(Camera):
raise DeviceAlreadyConnectedError(f"RealSenseCamera({self.serial_number}) is already connected.")
if self.mock:
import tests.mock_pyrealsense2 as rs
import tests.cameras.mock_pyrealsense2 as rs
else:
import pyrealsense2 as rs
@@ -372,7 +372,7 @@ class RealSenseCamera(Camera):
)
if self.mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2

8
lerobot/common/cameras/opencv/camera_opencv.py
View File

@@ -81,7 +81,7 @@ def _find_cameras(
possible_camera_ids: list[int | str], raise_when_empty=False, mock=False
) -> list[int | str]:
if mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2
@@ -270,7 +270,7 @@ class OpenCVCamera(Camera):
self.logs = {}
if self.mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2
@@ -287,7 +287,7 @@ class OpenCVCamera(Camera):
raise DeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
if self.mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2
@@ -399,7 +399,7 @@ class OpenCVCamera(Camera):
# so we convert the image color from BGR to RGB.
if requested_color_mode == "rgb":
if self.mock:
import tests.mock_cv2 as cv2
import tests.cameras.mock_cv2 as cv2
else:
import cv2

12
lerobot/common/motors/dynamixel/dynamixel.py
View File

@@ -332,7 +332,7 @@ class DynamixelMotorsBus:
)
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl
@@ -356,7 +356,7 @@ class DynamixelMotorsBus:
def reconnect(self):
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl
@@ -646,7 +646,7 @@ class DynamixelMotorsBus:
def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl
@@ -691,7 +691,7 @@ class DynamixelMotorsBus:
start_time = time.perf_counter()
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl
@@ -757,7 +757,7 @@ class DynamixelMotorsBus:
def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl
@@ -793,7 +793,7 @@ class DynamixelMotorsBus:
start_time = time.perf_counter()
if self.mock:
import tests.mock_dynamixel_sdk as dxl
import tests.motors.mock_dynamixel_sdk as dxl
else:
import dynamixel_sdk as dxl

12
lerobot/common/motors/feetech/feetech.py
View File

@@ -313,7 +313,7 @@ class FeetechMotorsBus:
)
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs
@@ -337,7 +337,7 @@ class FeetechMotorsBus:
def reconnect(self):
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs
@@ -664,7 +664,7 @@ class FeetechMotorsBus:
def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs
@@ -702,7 +702,7 @@ class FeetechMotorsBus:
def read(self, data_name, motor_names: str | list[str] | None = None):
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs
@@ -782,7 +782,7 @@ class FeetechMotorsBus:
def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs
@@ -818,7 +818,7 @@ class FeetechMotorsBus:
start_time = time.perf_counter()
if self.mock:
import tests.mock_scservo_sdk as scs
import tests.motors.mock_scservo_sdk as scs
else:
import scservo_sdk as scs

40
lerobot/common/robots/lekiwi/README.md
View File

@@ -366,8 +366,8 @@ Now we have to calibrate the leader arm and the follower arm. The wheel motors d
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/lekiwi/mobile_calib_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
Make sure the arm is connected to the Raspberry Pi and run this script (on the Raspberry Pi) to launch manual calibration:
@@ -385,8 +385,8 @@ If you have the **wired** LeKiwi version please run all commands including this
### Calibrate leader arm
Then to calibrate the leader arm (which is attached to the laptop/pc). You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
Run this script (on your laptop/pc) to launch manual calibration:
@@ -416,22 +416,22 @@ python lerobot/scripts/control_robot.py \
You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
|------------|-------------------|-----------------------|
| Fast | 0.4 | 90 |
| Medium | 0.25 | 60 |
| Slow | 0.1 | 30 |
| ---------- | ------------------ | ---------------------- |
| Fast | 0.4 | 90 |
| Medium | 0.25 | 60 |
| Slow | 0.1 | 30 |
| Key | Action |
|------|--------------------------------|
| W | Move forward |
| A | Move left |
| S | Move backward |
| D | Move right |
| Z | Turn left |
| X | Turn right |
| R | Increase speed |
| F | Decrease speed |
| Key | Action |
| --- | -------------- |
| W | Move forward |
| A | Move left |
| S | Move backward |
| D | Move right |
| Z | Turn left |
| X | Turn right |
| R | Increase speed |
| F | Decrease speed |
> [!TIP]
> If you use a different keyboard you can change the keys for each command in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
@@ -549,14 +549,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_lekiwi_test \
--job_name=act_lekiwi_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_lekiwi_test/checkpoints`.

12
lerobot/common/robots/moss/README.md
View File

@@ -176,8 +176,8 @@ Next, you'll need to calibrate your Moss v1 robot to ensure that the leader and
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
Make sure both arms are connected and run this script to launch manual calibration:
@@ -192,8 +192,8 @@ python lerobot/scripts/control_robot.py \
**Manual calibration of leader arm**
Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
@@ -293,14 +293,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_moss_test \
--job_name=act_moss_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.

12
lerobot/common/robots/so100/README.md
View File

@@ -454,8 +454,8 @@ Next, you'll need to calibrate your SO-100 robot to ensure that the leader and f
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/follower_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/so100/follower_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/so100/follower_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
Make sure both arms are connected and run this script to launch manual calibration:
@@ -470,8 +470,8 @@ python lerobot/scripts/control_robot.py \
#### b. Manual calibration of leader arm
Follow step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
@@ -571,14 +571,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_so100_test \
--job_name=act_so100_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.

4
lerobot/common/robots/viperx/README.md
View File

@@ -135,14 +135,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_aloha_test \
--job_name=act_aloha_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)

14
lerobot/scripts/visualize_dataset.py
View File

@@ -265,13 +265,25 @@ def main():
),
)
parser.add_argument(
"--tolerance-s",
type=float,
default=1e-4,
help=(
"Tolerance in seconds used to ensure data timestamps respect the dataset fps value"
"This is argument passed to the constructor of LeRobotDataset and maps to its tolerance_s constructor argument"
"If not given, defaults to 1e-4."
),
)
args = parser.parse_args()
kwargs = vars(args)
repo_id = kwargs.pop("repo_id")
root = kwargs.pop("root")
tolerance_s = kwargs.pop("tolerance_s")
logging.info("Loading dataset")
dataset = LeRobotDataset(repo_id, root=root)
dataset = LeRobotDataset(repo_id, root=root, tolerance_s=tolerance_s)
visualize_dataset(dataset, **vars(args))

18
lerobot/scripts/visualize_dataset_html.py
View File

@@ -446,15 +446,31 @@ def main():
help="Delete the output directory if it exists already.",
)
parser.add_argument(
"--tolerance-s",
type=float,
default=1e-4,
help=(
"Tolerance in seconds used to ensure data timestamps respect the dataset fps value"
"This is argument passed to the constructor of LeRobotDataset and maps to its tolerance_s constructor argument"
"If not given, defaults to 1e-4."
),
)
args = parser.parse_args()
kwargs = vars(args)
repo_id = kwargs.pop("repo_id")
load_from_hf_hub = kwargs.pop("load_from_hf_hub")
root = kwargs.pop("root")
tolerance_s = kwargs.pop("tolerance_s")
dataset = None
if repo_id:
dataset = LeRobotDataset(repo_id, root=root) if not load_from_hf_hub else get_dataset_info(repo_id)
dataset = (
LeRobotDataset(repo_id, root=root, tolerance_s=tolerance_s)
if not load_from_hf_hub
else get_dataset_info(repo_id)
)
visualize_dataset_html(dataset, **vars(args))