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Fix typos (#1070)

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omahs
2025-05-05 10:35:32 +02:00
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README.md
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@@ -221,7 +221,7 @@ dataset attributes:
│ ├ episode_index (int64): index of the episode for this sample
│ ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
│ ├ timestamp (float32): timestamp in the episode
│ ├ next.done (bool): indicates the end of en episode ; True for the last frame in each episode
│ ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
│ └ index (int64): general index in the whole dataset
├ episode_data_index: contains 2 tensors with the start and end indices of each episode
│ ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0
@@ -270,7 +270,7 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
### Train your own policy
Check out [example 3](./examples/3_train_policy.py) that illustrate how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`.
@@ -321,7 +321,7 @@ Once you have trained a policy you may upload it to the Hugging Face hub using a
You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
- `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
- `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
- `train_config.json`: A consolidated configuration containing all parameter userd for training. The policy configuration should match `config.json` exactly. Thisis useful for anyone who wants to evaluate your policy or for reproducibility.
- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
To upload these to the hub, run the following:
```bash

2
docs/source/assemble_so101.mdx
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@@ -194,7 +194,7 @@ Here is a video of the process:
</div>
### Clean Parts
Remove all support material from the 3D-printed parts, the easiest wat to do this is using a small screwdriver to get underneath the support material.
Remove all support material from the 3D-printed parts, the easiest way to do this is using a small screwdriver to get underneath the support material.
### Joint 1

6
docs/source/getting_started_real_world_robot.mdx
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@@ -152,7 +152,7 @@ If everything is set up correctly, you can proceed with the rest of the tutorial
## Teleoperate with cameras
We can now teleoperate again while at the same time visualzing the camera's and joint positions with `rerun`.
We can now teleoperate again while at the same time visualizing the camera's and joint positions with `rerun`.
```bash
python lerobot/scripts/control_robot.py \
@@ -165,7 +165,7 @@ python lerobot/scripts/control_robot.py \
Once you're familiar with teleoperation, you can record your first dataset with SO-101.
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you've can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
Add your token to the cli by running this command:
```bash
@@ -318,7 +318,7 @@ python lerobot/scripts/train.py \
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.

2
examples/10_use_so100.md
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@@ -578,7 +578,7 @@ python lerobot/scripts/train.py \
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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.

4
examples/11_use_lekiwi.md
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@@ -134,7 +134,7 @@ First we will assemble the two SO100 arms. One to attach to the mobile base and
## SO100 Arms
### Configure motors
The instructions for configuring the motors can be found [Here](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#c-configure-the-motors) in step C of the SO100 tutorial. Besides the ID's for the arm motors we also need to set the motor ID's for the mobile base. These needs to be in a specific order to work. Below an image of the motor ID's and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ID's for the wheels are 7, 8 and 9.
The instructions for configuring the motors can be found [Here](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#c-configure-the-motors) in step C of the SO100 tutorial. Besides the ID's for the arm motors we also need to set the motor ID's for the mobile base. These need to be in a specific order to work. Below an image of the motor ID's and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ID's for the wheels are 7, 8 and 9.
<img src="../media/lekiwi/motor_ids.webp?raw=true" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
@@ -567,7 +567,7 @@ python lerobot/scripts/train.py \
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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.

6
examples/11_use_moss.md
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@@ -44,7 +44,7 @@ cd ~/lerobot && pip install -e ".[feetech]"
## Configure the motors
Follow steps 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the use of our scripts below.
Follow step 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the use of our scripts below.
**Find USB ports associated to your arms**
To find the correct ports for each arm, run the utility script twice:
@@ -164,7 +164,7 @@ Try to avoid rotating the motor while doing so to keep position 2048 set during
## Assemble the arms
Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). The first arm should take a bit more than 1 hour to assemble, but once you get use to it, you can do it under 1 hour for the second arm.
Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). The first arm should take a bit more than 1 hour to assemble, but once you get used to it, you can do it under 1 hour for the second arm.
## Calibrate
@@ -301,7 +301,7 @@ python lerobot/scripts/train.py \
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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.

6
examples/12_use_so101.md
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@@ -428,7 +428,7 @@ camera_01_frame_000047.png
Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
Now that you have the camera indexes, you should change then in the config. You can also change the fps, width or height of the camera.
Now that you have the camera indexes, you should change them in the config. You can also change the fps, width or height of the camera.
The camera config is defined per robot, can be found here [`RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py) and looks like this:
```python
@@ -515,7 +515,7 @@ If you have an additional camera you can add a wrist camera to the SO101. There
## Teleoperate with cameras
We can now teleoperate again while at the same time visualzing the camera's and joint positions with `rerun`.
We can now teleoperate again while at the same time visualizing the camera's and joint positions with `rerun`.
```bash
python lerobot/scripts/control_robot.py \
@@ -528,7 +528,7 @@ python lerobot/scripts/control_robot.py \
Once you're familiar with teleoperation, you can record your first dataset with SO-100.
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you've can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
Add your token to the cli by running this command:
```bash

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examples/2_evaluate_pretrained_policy.py
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@@ -13,7 +13,7 @@
# limitations under the License.
"""
This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
It requires the installation of the 'gym_pusht' simulation environment. Install it by running:
@@ -119,7 +119,7 @@ while not done:
rewards.append(reward)
frames.append(env.render())
# The rollout is considered done when the success state is reach (i.e. terminated is True),
# The rollout is considered done when the success state is reached (i.e. terminated is True),
# or the maximum number of iterations is reached (i.e. truncated is True)
done = terminated | truncated | done
step += 1

2
examples/3_train_policy.py
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@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
"""This script demonstrates how to train Diffusion Policy on the PushT environment.
Once you have trained a model with this script, you can try to evaluate it on
examples/2_evaluate_pretrained_policy.py

8
examples/4_train_policy_with_script.md
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@@ -1,5 +1,5 @@
This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run.
> **Note:** The following assume you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
## The training script
@@ -23,7 +23,7 @@ def train(cfg: TrainPipelineConfig):
You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated for this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes:
```python
@@ -43,7 +43,7 @@ class DatasetConfig:
```
This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`.
From the command line, we can specify this value with using a very similar syntax `--dataset.repo_id=repo/id`.
From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`.
By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified.
@@ -135,7 +135,7 @@ will start a training run with the same configuration used for training [lerobot
## Resume training
Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to that here.
Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here.
Let's reuse the command from the previous run and add a few more options:
```bash

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examples/7_get_started_with_real_robot.md
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@@ -377,7 +377,7 @@ robot = ManipulatorRobot(robot_config)
The `KochRobotConfig` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.
For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
**Calibrate and Connect the ManipulatorRobot**
@@ -399,7 +399,7 @@ And here are the corresponding positions for the leader arm:
You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.
During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask yo to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask you to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
Finally, the rest position ensures that the follower and leader arms are roughly aligned after calibration, preventing sudden movements that could damage the motors when starting teleoperation.
@@ -622,7 +622,7 @@ camera_01_frame_000047.png
Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
Finally, run this code to instantiate and connectyour camera:
Finally, run this code to instantiate and connect your camera:
```python
from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera

2
examples/8_use_stretch.md
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@@ -99,7 +99,7 @@ This is equivalent to running `stretch_robot_home.py`
> **Note:** If you run any of the LeRobot scripts below and Stretch is not properly homed, it will automatically home/calibrate first.
**Teleoperate**
Before trying teleoperation, you need activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
Before trying teleoperation, you need to activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
Now try out teleoperation (see above documentation to learn about the gamepad controls):

2
examples/9_use_aloha.md
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@@ -142,7 +142,7 @@ python lerobot/scripts/train.py \
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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.

2
examples/advanced/2_calculate_validation_loss.py
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@@ -66,7 +66,7 @@ def main():
print(f"Number of episodes in full dataset: {total_episodes}")
print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
# - Load train an val datasets
# - Load train and val datasets
train_dataset = LeRobotDataset(
"lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
)

2
lerobot/common/datasets/factory.py
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@@ -49,7 +49,7 @@ def resolve_delta_timestamps(
"observation.state": [-0.04, -0.02, 0]
"observation.action": [-0.02, 0, 0.02]
}
returns `None` if the the resulting dict is empty.
returns `None` if the resulting dict is empty.
"""
delta_timestamps = {}
for key in ds_meta.features:

2
lerobot/common/datasets/transforms.py
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@@ -128,7 +128,7 @@ class SharpnessJitter(Transform):
raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
if not 0.0 <= sharpness[0] <= sharpness[1]:
raise ValueError(f"sharpnesss values should be between (0., inf), but got {sharpness}.")
raise ValueError(f"sharpness values should be between (0., inf), but got {sharpness}.")
return float(sharpness[0]), float(sharpness[1])

4
lerobot/scripts/eval.py
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@@ -94,8 +94,8 @@ def rollout(
data will probably need to be discarded (for environments that aren't the first one to be done).
The return dictionary contains:
(optional) "observation": A a dictionary of (batch, sequence + 1, *) tensors mapped to observation
keys. NOTE the that this has an extra sequence element relative to the other keys in the
(optional) "observation": A dictionary of (batch, sequence + 1, *) tensors mapped to observation
keys. NOTE that this has an extra sequence element relative to the other keys in the
dictionary. This is because an extra observation is included for after the environment is
terminated or truncated.
"action": A (batch, sequence, action_dim) tensor of actions applied based on the observations (not