Add typos checks (#770)

.github/workflows/quality.yml

@@ -42,3 +42,15 @@ jobs:
 
       - name: Ruff format
         run: ruff format --diff
+
+  typos:
+    name: Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+
+      - name: typos-action
+        uses: crate-ci/typos@v1.29.10

.pre-commit-config.yaml

@@ -13,6 +13,11 @@ repos:
       - id: check-toml
       - id: end-of-file-fixer
       - id: trailing-whitespace
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.29.10
+    hooks:
+      - id: typos
+        args: [--force-exclude]
   - repo: https://github.com/asottile/pyupgrade
     rev: v3.19.1
     hooks:

CONTRIBUTING.md

@@ -228,7 +228,7 @@ Follow these steps to start contributing:
    git commit
    ```
 
-   Note, if you already commited some changes that have a wrong formatting, you can use:
+   Note, if you already committed some changes that have a wrong formatting, you can use:
    ```bash
    pre-commit run --all-files
    ```

benchmarks/video/README.md

@@ -114,7 +114,7 @@ We tried to measure the most impactful parameters for both encoding and decoding
 
 Additional encoding parameters exist that are not included in this benchmark. In particular:
 - `-preset` which allows for selecting encoding presets. This represents a collection of options that will provide a certain encoding speed to compression ratio. By leaving this parameter unspecified, it is considered to be `medium` for libx264 and libx265 and `8` for libsvtav1.
-- `-tune` which allows to optimize the encoding for certains aspects (e.g. film quality, fast decoding, etc.).
+- `-tune` which allows to optimize the encoding for certain aspects (e.g. film quality, fast decoding, etc.).
 
 See the documentation mentioned above for more detailed info on these settings and for a more comprehensive list of other parameters.
 

examples/11_use_lekiwi.md

@@ -185,7 +185,7 @@ sudo chmod 666 /dev/ttyACM1
 
 #### d. Update config file
 
-IMPORTANTLY: Now that you have your ports of leader and follower arm and ip adress of the mobile-so100, update the **ip** in Network configuration, **port** in leader_arms and **port** in lekiwi. In the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
+IMPORTANTLY: Now that you have your ports of leader and follower arm and ip address of the mobile-so100, update the **ip** in Network configuration, **port** in leader_arms and **port** in lekiwi. In the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
 ```python
 @RobotConfig.register_subclass("lekiwi")
 @dataclass
@@ -324,7 +324,7 @@ You should see on your laptop something like this: ```[INFO] Connected to remote
 | F    | Decrease speed                  |
 
 > [!TIP]
->  If you use a different keyboard you can change the keys for each commmand in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
+>  If you use a different keyboard you can change the keys for each command in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
 
 ## Troubleshoot communication
 

examples/11_use_moss.md

@@ -2,7 +2,7 @@ This tutorial explains how to use [Moss v1](https://github.com/jess-moss/moss-ro
 
 ## Source the parts
 
-Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials, with link to source the parts, as well as the instructions to 3D print the parts, and advices if it's your first time printing or if you don't own a 3D printer already.
+Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials with link to source the parts, as well as the instructions to 3D print the parts and advice if it's your first time printing or if you don't own a 3D printer already.
 
 **Important**: Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
 

examples/7_get_started_with_real_robot.md

@@ -398,7 +398,7 @@ 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 mesure 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 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.
 
 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.
 
@@ -663,7 +663,7 @@ camera.disconnect()
 
 **Instantiate your robot with cameras**
 
-Additionaly, you can set up your robot to work with your cameras.
+Additionally, you can set up your robot to work with your cameras.
 
 Modify the following Python code with the appropriate camera names and configurations:
 ```python
@@ -825,8 +825,8 @@ It contains:
 - `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.
 - `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.
 - `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.
-- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchrously.
-- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchrously.
+- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.
+- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
 
 Troubleshooting:
 - On Linux, if you encounter a hanging issue when using cameras, uninstall opencv and re-install it with conda:
@@ -846,7 +846,7 @@ At the end of data recording, your dataset will be uploaded on your Hugging Face
 echo https://huggingface.co/datasets/${HF_USER}/koch_test
 ```
 
-### b. Advices for recording dataset
+### b. Advice for recording dataset
 
 Once you're comfortable with data recording, it's time to create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings.
 

examples/8_use_stretch.md

@@ -98,7 +98,7 @@ python lerobot/scripts/control_robot.py \
 ```
 This is equivalent to running `stretch_robot_home.py`
 
-> **Note:** If you run any of the LeRobot scripts below and Stretch is not poperly homed, it will automatically home/calibrate first.
+> **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).

examples/9_use_aloha.md

@@ -172,10 +172,10 @@ python lerobot/scripts/control_robot.py \
 As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
 1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_aloha_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_aloha_test`).
 2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_aloha_test`).
-3. We use `--control.num_image_writer_processes=1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constent 30 fps during inference. Feel free to explore different values for `--control.num_image_writer_processes`.
+3. We use `--control.num_image_writer_processes=1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constant 30 fps during inference. Feel free to explore different values for `--control.num_image_writer_processes`.
 
 ## More
 
-Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explaination.
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explanation.
 
 If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`.

lerobot/common/datasets/compute_stats.py

@@ -92,7 +92,7 @@ def compute_episode_stats(episode_data: dict[str, list[str] | np.ndarray], featu
             axes_to_reduce = (0, 2, 3)  # keep channel dim
             keepdims = True
         else:
-            ep_ft_array = data  # data is alreay a np.ndarray
+            ep_ft_array = data  # data is already a np.ndarray
             axes_to_reduce = 0  # compute stats over the first axis
             keepdims = data.ndim == 1  # keep as np.array
 

lerobot/common/datasets/lerobot_dataset.py

@@ -226,7 +226,7 @@ class LeRobotDatasetMetadata:
 
     def add_task(self, task: str):
         """
-        Given a task in natural language, add it to the dictionnary of tasks.
+        Given a task in natural language, add it to the dictionary of tasks.
         """
         if task in self.task_to_task_index:
             raise ValueError(f"The task '{task}' already exists and can't be added twice.")
@@ -389,7 +389,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 - info contains various information about the dataset like shapes, keys, fps etc.
                 - stats stores the dataset statistics of the different modalities for normalization
                 - tasks contains the prompts for each task of the dataset, which can be used for
-                  task-conditionned training.
+                  task-conditioned training.
             - hf_dataset (from datasets.Dataset), which will read any values from parquet files.
             - videos (optional) from which frames are loaded to be synchronous with data from parquet files.
 
@@ -848,7 +848,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         episode_buffer["index"] = np.arange(self.meta.total_frames, self.meta.total_frames + episode_length)
         episode_buffer["episode_index"] = np.full((episode_length,), episode_index)
 
-        # Add new tasks to the tasks dictionnary
+        # Add new tasks to the tasks dictionary
         for task in episode_tasks:
             task_index = self.meta.get_task_index(task)
             if task_index is None:

lerobot/common/datasets/push_dataset_to_hub/_download_raw.py

@@ -152,7 +152,7 @@ def download_raw(raw_dir: Path, repo_id: str):
             stacklevel=1,
         )
 
-    # Send warning if raw_dir isn't well formated
+    # Send warning if raw_dir isn't well formatted
     if raw_dir.parts[-2] != user_id or raw_dir.parts[-1] != dataset_id:
         warnings.warn(
             f"""`raw_dir` ({raw_dir}) doesn't contain a community or user id `/` the name of the dataset that

lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py

@@ -68,9 +68,9 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod
             modality_df,
             on="timestamp_utc",
             # "nearest" is the best option over "backward", since the latter can desynchronizes camera timestamps by
-            # matching timestamps that are too far appart, in order to fit the backward constraints. It's not the case for "nearest".
+            # matching timestamps that are too far apart, in order to fit the backward constraints. It's not the case for "nearest".
             # However, note that "nearest" might synchronize the reference camera with other cameras on slightly future timestamps.
-            # are too far appart.
+            # are too far apart.
             direction="nearest",
             tolerance=pd.Timedelta(f"{1 / fps} seconds"),
         )
@@ -126,7 +126,7 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod
     videos_dir.parent.mkdir(parents=True, exist_ok=True)
     videos_dir.symlink_to((raw_dir / "videos").absolute())
 
-    # sanity check the video paths are well formated
+    # sanity check the video paths are well formatted
     for key in df:
         if "observation.images." not in key:
             continue
@@ -143,7 +143,7 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod
             # it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
             data_dict[key] = [video_frame[0] for video_frame in df[key].values]
 
-            # sanity check the video path is well formated
+            # sanity check the video path is well formatted
             video_path = videos_dir.parent / data_dict[key][0]["path"]
             if not video_path.exists():
                 raise ValueError(f"Video file not found in {video_path}")

lerobot/common/datasets/push_dataset_to_hub/openx_rlds_format.py

@@ -17,7 +17,7 @@
 For all datasets in the RLDS format.
 For https://github.com/google-deepmind/open_x_embodiment (OPENX) datasets.
 
-NOTE: You need to install tensorflow and tensorflow_datsets before running this script.
+NOTE: You need to install tensorflow and tensorflow_datasets before running this script.
 
 Example:
     python lerobot/scripts/push_dataset_to_hub.py \

lerobot/common/datasets/utils.py

@@ -222,7 +222,7 @@ def load_episodes(local_dir: Path) -> dict:
 
 
 def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path):
-    # We wrap episode_stats in a dictionnary since `episode_stats["episode_index"]`
+    # We wrap episode_stats in a dictionary since `episode_stats["episode_index"]`
     # is a dictionary of stats and not an integer.
     episode_stats = {"episode_index": episode_index, "stats": serialize_dict(episode_stats)}
     append_jsonlines(episode_stats, local_dir / EPISODES_STATS_PATH)
@@ -445,10 +445,10 @@ def get_episode_data_index(
     if episodes is not None:
         episode_lengths = {ep_idx: episode_lengths[ep_idx] for ep_idx in episodes}
 
-    cumulative_lenghts = list(accumulate(episode_lengths.values()))
+    cumulative_lengths = list(accumulate(episode_lengths.values()))
     return {
-        "from": torch.LongTensor([0] + cumulative_lenghts[:-1]),
-        "to": torch.LongTensor(cumulative_lenghts),
+        "from": torch.LongTensor([0] + cumulative_lengths[:-1]),
+        "to": torch.LongTensor(cumulative_lengths),
     }
 
 

lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py

@@ -31,6 +31,7 @@ from lerobot.common.robot_devices.robots.configs import AlohaRobotConfig
 
 LOCAL_DIR = Path("data/")
 
+# spellchecker:off
 ALOHA_MOBILE_INFO = {
     "robot_config": AlohaRobotConfig(),
     "license": "mit",
@@ -856,6 +857,7 @@ DATASETS = {
             }""").lstrip(),
     },
 }
+# spellchecker:on
 
 
 def batch_convert():

lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py

@@ -17,7 +17,7 @@
 """
 This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 1.6 to
 2.0. You will be required to provide the 'tasks', which is a short but accurate description in plain English
-for each of the task performed in the dataset. This will allow to easily train models with task-conditionning.
+for each of the task performed in the dataset. This will allow to easily train models with task-conditioning.
 
 We support 3 different scenarios for these tasks (see instructions below):
     1. Single task dataset: all episodes of your dataset have the same single task.

lerobot/common/datasets/video_utils.py

@@ -73,7 +73,7 @@ def decode_video_frames_torchvision(
     last_ts = max(timestamps)
 
     # access closest key frame of the first requested frame
-    # Note: closest key frame timestamp is usally smaller than `first_ts` (e.g. key frame can be the first frame of the video)
+    # Note: closest key frame timestamp is usually smaller than `first_ts` (e.g. key frame can be the first frame of the video)
     # for details on what `seek` is doing see: https://pyav.basswood-io.com/docs/stable/api/container.html?highlight=inputcontainer#av.container.InputContainer.seek
     reader.seek(first_ts, keyframes_only=keyframes_only)
 

lerobot/common/envs/factory.py

@@ -37,12 +37,12 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
     Args:
         cfg (EnvConfig): the config of the environment to instantiate.
         n_envs (int, optional): The number of parallelized env to return. Defaults to 1.
-        use_async_envs (bool, optional): Wether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to
+        use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to
             False.
 
     Raises:
         ValueError: if n_envs < 1
-        ModuleNotFoundError: If the requested env package is not intalled
+        ModuleNotFoundError: If the requested env package is not installed
 
     Returns:
         gym.vector.VectorEnv: The parallelized gym.env instance.

lerobot/common/policies/act/configuration_act.py

@@ -64,7 +64,7 @@ class ACTConfig(PreTrainedConfig):
         output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
             original scale. Note that this is also used for normalizing the training targets.
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
-        pretrained_backbone_weights: Pretrained weights from torchvision to initalize the backbone.
+        pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         replace_final_stride_with_dilation: Whether to replace the ResNet's final 2x2 stride with a dilated
             convolution.

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -68,7 +68,7 @@ class DiffusionConfig(PreTrainedConfig):
             within the image size. If None, no cropping is done.
         crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
             mode).
-        pretrained_backbone_weights: Pretrained weights from torchvision to initalize the backbone.
+        pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
             The group sizes are set to be about 16 (to be precise, feature_dim // 16).
@@ -99,7 +99,7 @@ class DiffusionConfig(PreTrainedConfig):
         num_inference_steps: Number of reverse diffusion steps to use at inference time (steps are evenly
             spaced). If not provided, this defaults to be the same as `num_train_timesteps`.
         do_mask_loss_for_padding: Whether to mask the loss when there are copy-padded actions. See
-            `LeRobotDataset` and `load_previous_and_future_frames` for mor information. Note, this defaults
+            `LeRobotDataset` and `load_previous_and_future_frames` for more information. Note, this defaults
             to False as the original Diffusion Policy implementation does the same.
     """
 

lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py

@@ -2,7 +2,7 @@
 Convert pi0 parameters from Jax to Pytorch
 
 Follow [README of openpi](https://github.com/Physical-Intelligence/openpi) to create a new environment
-and install the required librairies.
+and install the required libraries.
 
 ```bash
 cd ~/code/openpi

lerobot/common/policies/tdmpc/configuration_tdmpc.py

@@ -76,7 +76,7 @@ class TDMPCConfig(PreTrainedConfig):
         n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can
             be zero.
         uncertainty_regularizer_coeff: Coefficient for the uncertainty regularization used when estimating
-            trajectory values (this is the λ coeffiecient in eqn 4 of FOWM).
+            trajectory values (this is the λ coefficient in eqn 4 of FOWM).
         n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration.
         elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the
             elites, when updating the gaussian parameters for CEM.
@@ -165,7 +165,7 @@ class TDMPCConfig(PreTrainedConfig):
         """Input validation (not exhaustive)."""
         if self.n_gaussian_samples <= 0:
             raise ValueError(
-                f"The number of guassian samples for CEM should be non-zero. Got `{self.n_gaussian_samples=}`"
+                f"The number of gaussian samples for CEM should be non-zero. Got `{self.n_gaussian_samples=}`"
             )
         if self.normalization_mapping["ACTION"] is not NormalizationMode.MIN_MAX:
             raise ValueError(

lerobot/common/policies/vqbet/configuration_vqbet.py

@@ -66,7 +66,7 @@ class VQBeTConfig(PreTrainedConfig):
             within the image size. If None, no cropping is done.
         crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
             mode).
-        pretrained_backbone_weights: Pretrained weights from torchvision to initalize the backbone.
+        pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
             The group sizes are set to be about 16 (to be precise, feature_dim // 16).

lerobot/common/policies/vqbet/modeling_vqbet.py

@@ -485,7 +485,7 @@ class VQBeTHead(nn.Module):
     def forward(self, x, **kwargs) -> dict:
         # N is the batch size, and T is number of action query tokens, which are process through same GPT
         N, T, _ = x.shape
-        # we calculate N and T side parallely. Thus, the dimensions would be
+        # we calculate N and T side parallelly. Thus, the dimensions would be
         # (batch size * number of action query tokens, action chunk size, action dimension)
         x = einops.rearrange(x, "N T WA -> (N T) WA")
 
@@ -772,7 +772,7 @@ class VqVae(nn.Module):
         Encoder and decoder are MLPs consisting of an input, output layer, and hidden layer, respectively.
         The vq_layer uses residual VQs.
 
-        This class contains functions for training the encoder and decoder along with the residual VQ layer (for trainign phase 1),
+        This class contains functions for training the encoder and decoder along with the residual VQ layer (for training phase 1),
         as well as functions to help BeT training part in training phase 2.
         """
 

lerobot/common/policies/vqbet/vqbet_utils.py

@@ -38,7 +38,7 @@ from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 This file is part of a VQ-BeT that utilizes code from the following repositories:
 
     - Vector Quantize PyTorch code is licensed under the MIT License:
-        Origianl source: https://github.com/lucidrains/vector-quantize-pytorch
+        Original source: https://github.com/lucidrains/vector-quantize-pytorch
 
     - nanoGPT part is an adaptation of Andrej Karpathy's nanoGPT implementation in PyTorch.
         Original source: https://github.com/karpathy/nanoGPT
@@ -289,7 +289,7 @@ class GPT(nn.Module):
 This file is a part for Residual Vector Quantization that utilizes code from the following repository:
 
     - Phil Wang's vector-quantize-pytorch implementation in PyTorch.
-        Origianl source: https://github.com/lucidrains/vector-quantize-pytorch
+        Original source: https://github.com/lucidrains/vector-quantize-pytorch
 
     - The vector-quantize-pytorch code is licensed under the MIT License:
 
@@ -1349,9 +1349,9 @@ class EuclideanCodebook(nn.Module):
 
         # calculate distributed variance
 
-        variance_numer = reduce((data - batch_mean) ** 2, "h n d -> h 1 d", "sum")
-        distributed.all_reduce(variance_numer)
-        batch_variance = variance_numer / num_vectors
+        variance_number = reduce((data - batch_mean) ** 2, "h n d -> h 1 d", "sum")
+        distributed.all_reduce(variance_number)
+        batch_variance = variance_number / num_vectors
 
         self.update_with_decay("batch_variance", batch_variance, self.affine_param_batch_decay)
 

lerobot/common/robot_devices/control_configs.py

@@ -66,7 +66,7 @@ class RecordControlConfig(ControlConfig):
     private: bool = False
     # Add tags to your dataset on the hub.
     tags: list[str] | None = None
-    # Number of subprocesses handling the saving of frames as PNGs. Set to 0 to use threads only;
+    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
     # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
     # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
     # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.

lerobot/common/robot_devices/motors/dynamixel.py

@@ -242,7 +242,7 @@ class DriveMode(enum.Enum):
 class CalibrationMode(enum.Enum):
     # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
     DEGREE = 0
-    # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+    # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
     LINEAR = 1
 
 
@@ -610,7 +610,7 @@ class DynamixelMotorsBus:
                 # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
                 values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
 
-                # Substract the homing offsets to come back to actual motor range of values
+                # Subtract the homing offsets to come back to actual motor range of values
                 # which can be arbitrary.
                 values[i] -= homing_offset
 

lerobot/common/robot_devices/motors/feetech.py

@@ -221,7 +221,7 @@ class DriveMode(enum.Enum):
 class CalibrationMode(enum.Enum):
     # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
     DEGREE = 0
-    # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+    # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
     LINEAR = 1
 
 
@@ -591,7 +591,7 @@ class FeetechMotorsBus:
                 # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
                 values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
 
-                # Substract the homing offsets to come back to actual motor range of values
+                # Subtract the homing offsets to come back to actual motor range of values
                 # which can be arbitrary.
                 values[i] -= homing_offset
 
@@ -632,7 +632,7 @@ class FeetechMotorsBus:
                 track["prev"][idx] = values[i]
                 continue
 
-            # Detect a full rotation occured
+            # Detect a full rotation occurred
             if abs(track["prev"][idx] - values[i]) > 2048:
                 # Position went below 0 and got reset to 4095
                 if track["prev"][idx] < values[i]:

lerobot/common/robot_devices/robots/dynamixel_calibration.py

@@ -87,7 +87,7 @@ def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type
     # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
     # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
     # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarely rotate clockwise from the point of view
+    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
     # of the previous motor in the kinetic chain.
     print("\nMove arm to rotated target position")
     print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
@@ -115,7 +115,7 @@ def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type
 
     # TODO(rcadene): make type of joints (DEGREE or LINEAR) configurable from yaml?
     if robot_type in ["aloha"] and "gripper" in arm.motor_names:
-        # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+        # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
         calib_idx = arm.motor_names.index("gripper")
         calib_mode[calib_idx] = CalibrationMode.LINEAR.name
 

lerobot/common/robot_devices/robots/feetech_calibration.py

@@ -443,7 +443,7 @@ def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, a
     # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
     # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
     # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarely rotate clockwise from the point of view
+    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
     # of the previous motor in the kinetic chain.
     print("\nMove arm to rotated target position")
     print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))

lerobot/common/robot_devices/robots/manipulator.py

@@ -44,7 +44,7 @@ class ManipulatorRobot:
     # TODO(rcadene): Implement force feedback
     """This class allows to control any manipulator robot of various number of motors.
 
-    Non exaustive list of robots:
+    Non exhaustive list of robots:
     - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow expansion, developed
     by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
     - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
@@ -55,7 +55,7 @@ class ManipulatorRobot:
     robot = ManipulatorRobot(KochRobotConfig())
     ```
 
-    Example of overwritting motors during instantiation:
+    Example of overwriting motors during instantiation:
     ```python
     # Defines how to communicate with the motors of the leader and follower arms
     leader_arms = {
@@ -90,7 +90,7 @@ class ManipulatorRobot:
     robot = ManipulatorRobot(robot_config)
     ```
 
-    Example of overwritting cameras during instantiation:
+    Example of overwriting cameras during instantiation:
     ```python
     # Defines how to communicate with 2 cameras connected to the computer.
     # Here, the webcam of the laptop and the phone (connected in USB to the laptop)
@@ -348,7 +348,7 @@ class ManipulatorRobot:
             set_operating_mode_(self.follower_arms[name])
 
             # Set better PID values to close the gap between recorded states and actions
-            # TODO(rcadene): Implement an automatic procedure to set optimial PID values for each motor
+            # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor
             self.follower_arms[name].write("Position_P_Gain", 1500, "elbow_flex")
             self.follower_arms[name].write("Position_I_Gain", 0, "elbow_flex")
             self.follower_arms[name].write("Position_D_Gain", 600, "elbow_flex")
@@ -500,7 +500,7 @@ class ManipulatorRobot:
             self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
             self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
 
-        # Populate output dictionnaries
+        # Populate output dictionaries
         obs_dict, action_dict = {}, {}
         obs_dict["observation.state"] = state
         action_dict["action"] = action
@@ -540,7 +540,7 @@ class ManipulatorRobot:
             self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
             self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
 
-        # Populate output dictionnaries and format to pytorch
+        # Populate output dictionaries and format to pytorch
         obs_dict = {}
         obs_dict["observation.state"] = state
         for name in self.cameras:

lerobot/common/robot_devices/robots/stretch.py

@@ -108,7 +108,7 @@ class StretchRobot(StretchAPI):
             self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
             self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
 
-        # Populate output dictionnaries
+        # Populate output dictionaries
         obs_dict, action_dict = {}, {}
         obs_dict["observation.state"] = state
         action_dict["action"] = action
@@ -153,7 +153,7 @@ class StretchRobot(StretchAPI):
             self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
             self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
 
-        # Populate output dictionnaries
+        # Populate output dictionaries
         obs_dict = {}
         obs_dict["observation.state"] = state
         for name in self.cameras:

lerobot/configs/default.py

@@ -27,7 +27,7 @@ class DatasetConfig:
     # You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
     # keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
     # "dataset_index" into the returned item. The index mapping is made according to the order in which the
-    # datsets are provided.
+    # datasets are provided.
     repo_id: str
     # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | None = None

lerobot/configs/train.py

@@ -102,7 +102,7 @@ class TrainPipelineConfig(HubMixin):
 
         if not self.resume and isinstance(self.output_dir, Path) and self.output_dir.is_dir():
             raise FileExistsError(
-                f"Output directory {self.output_dir} alreay exists and resume is {self.resume}. "
+                f"Output directory {self.output_dir} already exists and resume is {self.resume}. "
                 f"Please change your output directory so that {self.output_dir} is not overwritten."
             )
         elif not self.output_dir:

lerobot/scripts/control_sim_robot.py

@@ -59,8 +59,8 @@ python lerobot/scripts/control_sim_robot.py record \
 ```
 
 **NOTE**: You can use your keyboard to control data recording flow.
-- Tap right arrow key '->' to early exit while recording an episode and go to reseting the environment.
-- Tap right arrow key '->' to early exit while reseting the environment and got to recording the next episode.
+- Tap right arrow key '->' to early exit while recording an episode and go to resetting the environment.
+- Tap right arrow key '->' to early exit while resetting the environment and got to recording the next episode.
 - Tap left arrow key '<-' to early exit and re-record the current episode.
 - Tap escape key 'esc' to stop the data recording.
 This might require a sudo permission to allow your terminal to monitor keyboard events.
@@ -131,7 +131,7 @@ def none_or_int(value):
 
 def init_sim_calibration(robot, cfg):
     # Constants necessary for transforming the joint pos of the real robot to the sim
-    # depending on the robot discription used in that sim.
+    # depending on the robot description used in that sim.
     start_pos = np.array(robot.leader_arms.main.calibration["start_pos"])
     axis_directions = np.array(cfg.get("axis_directions", [1]))
     offsets = np.array(cfg.get("offsets", [0])) * np.pi
@@ -445,7 +445,7 @@ if __name__ == "__main__":
         type=int,
         default=0,
         help=(
-            "Number of subprocesses handling the saving of frames as PNGs. Set to 0 to use threads only; "
+            "Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only; "
             "set to ≥1 to use subprocesses, each using threads to write images. The best number of processes "
             "and threads depends on your system. We recommend 4 threads per camera with 0 processes. "
             "If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses."

lerobot/scripts/push_dataset_to_hub.py

@@ -175,7 +175,7 @@ def push_dataset_to_hub(
         # Robustify when `local_dir` is str instead of Path
         local_dir = Path(local_dir)
 
-        # Send warning if local_dir isn't well formated
+        # Send warning if local_dir isn't well formatted
         if local_dir.parts[-2] != user_id or local_dir.parts[-1] != dataset_id:
             warnings.warn(
                 f"`local_dir` ({local_dir}) doesn't contain a community or user id `/` the name of the dataset that match the `repo_id` (e.g. 'data/lerobot/pusht'). Following this naming convention is advised, but not mandatory.",

lerobot/scripts/train.py

@@ -72,7 +72,7 @@ def update_policy(
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
     grad_scaler.scale(loss).backward()
 
-    # Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
+    # Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
     grad_scaler.unscale_(optimizer)
 
     grad_norm = torch.nn.utils.clip_grad_norm_(

lerobot/scripts/visualize_dataset_html.py

@@ -364,7 +364,7 @@ def visualize_dataset_html(
                 template_folder=template_dir,
             )
     else:
-        # Create a simlink from the dataset video folder containg mp4 files to the output directory
+        # Create a simlink from the dataset video folder containing mp4 files to the output directory
         # so that the http server can get access to the mp4 files.
         if isinstance(dataset, LeRobotDataset):
             ln_videos_dir = static_dir / "videos"

pyproject.toml

@@ -116,6 +116,19 @@ exclude = [
 select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
 
 
+[tool.typos]
+default.extend-ignore-re = [
+    "(?Rm)^.*(#|//)\\s*spellchecker:disable-line$",  # spellchecker:disable-line
+    "(?s)(#|//)\\s*spellchecker:off.*?\\n\\s*(#|//)\\s*spellchecker:on"  # spellchecker:<on|off>
+]
+default.extend-ignore-identifiers-re = [
+    # Add individual words here to ignore them
+    "2nd",
+    "pn",
+    "ser",
+    "ein",
+]
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

tests/mock_cv2.py

@@ -18,11 +18,11 @@ def _generate_image(width: int, height: int):
     return np.random.randint(0, 256, size=(height, width, 3), dtype=np.uint8)
 
 
-def cvtColor(color_image, color_convertion):  # noqa: N802
-    if color_convertion in [COLOR_RGB2BGR, COLOR_BGR2RGB]:
+def cvtColor(color_image, color_conversion):  # noqa: N802
+    if color_conversion in [COLOR_RGB2BGR, COLOR_BGR2RGB]:
         return color_image[:, :, [2, 1, 0]]
     else:
-        raise NotImplementedError(color_convertion)
+        raise NotImplementedError(color_conversion)
 
 
 def rotate(color_image, rotation):

tests/test_cameras.py

@@ -27,7 +27,7 @@ import pytest
 from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
 from tests.utils import TEST_CAMERA_TYPES, make_camera, require_camera
 
-# Maximum absolute difference between two consecutive images recored by a camera.
+# Maximum absolute difference between two consecutive images recorded by a camera.
 # This value differs with respect to the camera.
 MAX_PIXEL_DIFFERENCE = 25
 

tests/test_control_robot.py

@@ -179,7 +179,7 @@ def test_record_and_replay_and_policy(tmp_path, request, robot_type, mock):
     policy.save_pretrained(pretrained_policy_path)
 
     # In `examples/9_use_aloha.md`, we advise using `num_image_writer_processes=1`
-    # during inference, to reach constent fps, so we test this here.
+    # during inference, to reach constant fps, so we test this here.
     if robot_type == "aloha":
         num_image_writer_processes = 1
 

tests/test_datasets.py

@@ -486,7 +486,7 @@ def test_backward_compatibility(repo_id):
         old_frame = load_file(test_dir / f"frame_{i}.safetensors")  # noqa: B023
 
         # ignore language instructions (if exists) in language conditioned datasets
-        # TODO (michel-aractingi): transform language obs to langauge embeddings via tokenizer
+        # TODO (michel-aractingi): transform language obs to language embeddings via tokenizer
         new_frame.pop("language_instruction", None)
         old_frame.pop("language_instruction", None)
         new_frame.pop("task", None)

tests/test_delta_timestamps.py

@@ -32,10 +32,10 @@ def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, np.n
         ep_table = table.filter(pc.equal(table["episode_index"], ep_idx))
         episode_lengths.insert(ep_idx, len(ep_table))
 
-    cumulative_lenghts = list(accumulate(episode_lengths))
+    cumulative_lengths = list(accumulate(episode_lengths))
     return {
-        "from": np.array([0] + cumulative_lenghts[:-1], dtype=np.int64),
-        "to": np.array(cumulative_lenghts, dtype=np.int64),
+        "from": np.array([0] + cumulative_lengths[:-1], dtype=np.int64),
+        "to": np.array(cumulative_lengths, dtype=np.int64),
     }
 
 

tests/test_motors.py

@@ -88,7 +88,7 @@ def test_motors_bus(request, motor_type, mock):
 
     motors_bus = make_motors_bus(motor_type, mock=mock)
 
-    # Test reading and writting before connecting raises an error
+    # Test reading and writing before connecting raises an error
     with pytest.raises(RobotDeviceNotConnectedError):
         motors_bus.read("Torque_Enable")
     with pytest.raises(RobotDeviceNotConnectedError):

tests/test_robots.py

@@ -86,7 +86,7 @@ def test_robot(tmp_path, request, robot_type, mock):
     robot.connect()
     robot.teleop_step()
 
-    # Test data recorded during teleop are well formated
+    # Test data recorded during teleop are well formatted
     observation, action = robot.teleop_step(record_data=True)
     # State
     assert "observation.state" in observation