Simplify configs (#550)

Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: HUANG TZU-CHUN <137322177+tc-huang@users.noreply.github.com>

lerobot/common/datasets/factory.py

@@ -14,103 +14,102 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
+from pprint import pformat
 
 import torch
-from omegaconf import ListConfig, OmegaConf
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
-from lerobot.common.datasets.transforms import get_image_transforms
+from lerobot.common.datasets.lerobot_dataset import (
+    LeRobotDataset,
+    LeRobotDatasetMetadata,
+    MultiLeRobotDataset,
+)
+from lerobot.common.datasets.transforms import ImageTransforms
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+
+IMAGENET_STATS = {
+    "mean": [[[0.485]], [[0.456]], [[0.406]]],  # (c,1,1)
+    "std": [[[0.229]], [[0.224]], [[0.225]]],  # (c,1,1)
+}
 
 
-def resolve_delta_timestamps(cfg):
-    """Resolves delta_timestamps config key (in-place) by using `eval`.
+def resolve_delta_timestamps(
+    cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata
+) -> dict[str, list] | None:
+    """Resolves delta_timestamps by reading from the 'delta_indices' properties of the PreTrainedConfig.
 
-    Doesn't do anything if delta_timestamps is not specified or has already been resolve (as evidenced by
-    the data type of its values).
-    """
-    delta_timestamps = cfg.training.get("delta_timestamps")
-    if delta_timestamps is not None:
-        for key in delta_timestamps:
-            if isinstance(delta_timestamps[key], str):
-                # TODO(rcadene, alexander-soare): remove `eval` to avoid exploit
-                cfg.training.delta_timestamps[key] = eval(delta_timestamps[key])
-
-
-def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotDataset:
-    """
     Args:
-        cfg: A Hydra config as per the LeRobot config scheme.
-        split: Select the data subset used to create an instance of LeRobotDataset.
-            All datasets hosted on [lerobot](https://huggingface.co/lerobot) contain only one subset: "train".
-            Thus, by default, `split="train"` selects all the available data. `split` aims to work like the
-            slicer in the hugging face datasets:
-            https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
-            As of now, it only supports `split="train[:n]"` to load the first n frames of the dataset or
-            `split="train[n:]"` to load the last n frames. For instance `split="train[:1000]"`.
+        cfg (PreTrainedConfig): The PreTrainedConfig to read delta_indices from.
+        ds_meta (LeRobotDatasetMetadata): The dataset from which features and fps are used to build
+            delta_timestamps against.
+
     Returns:
-        The LeRobotDataset.
+        dict[str, list] | None: A dictionary of delta_timestamps, e.g.:
+            {
+                "observation.state": [-0.04, -0.02, 0]
+                "observation.action": [-0.02, 0, 0.02]
+            }
+            returns `None` if the the resulting dict is empty.
     """
-    if not isinstance(cfg.dataset_repo_id, (str, ListConfig)):
-        raise ValueError(
-            "Expected cfg.dataset_repo_id to be either a single string to load one dataset or a list of "
-            "strings to load multiple datasets."
-        )
+    delta_timestamps = {}
+    for key in ds_meta.features:
+        if key == "next.reward" and cfg.reward_delta_indices is not None:
+            delta_timestamps[key] = [i / ds_meta.fps for i in cfg.reward_delta_indices]
+        if key == "action" and cfg.action_delta_indices is not None:
+            delta_timestamps[key] = [i / ds_meta.fps for i in cfg.action_delta_indices]
+        if key.startswith("observation.") and cfg.observation_delta_indices is not None:
+            delta_timestamps[key] = [i / ds_meta.fps for i in cfg.observation_delta_indices]
 
-    # A soft check to warn if the environment matches the dataset. Don't check if we are using a real world env (dora).
-    if cfg.env.name != "dora":
-        if isinstance(cfg.dataset_repo_id, str):
-            dataset_repo_ids = [cfg.dataset_repo_id]  # single dataset
-        else:
-            dataset_repo_ids = cfg.dataset_repo_id  # multiple datasets
+    if len(delta_timestamps) == 0:
+        delta_timestamps = None
 
-        for dataset_repo_id in dataset_repo_ids:
-            if cfg.env.name not in dataset_repo_id:
-                logging.warning(
-                    f"There might be a mismatch between your training dataset ({dataset_repo_id=}) and your "
-                    f"environment ({cfg.env.name=})."
-                )
+    return delta_timestamps
 
-    resolve_delta_timestamps(cfg)
 
-    image_transforms = None
-    if cfg.training.image_transforms.enable:
-        cfg_tf = cfg.training.image_transforms
-        image_transforms = get_image_transforms(
-            brightness_weight=cfg_tf.brightness.weight,
-            brightness_min_max=cfg_tf.brightness.min_max,
-            contrast_weight=cfg_tf.contrast.weight,
-            contrast_min_max=cfg_tf.contrast.min_max,
-            saturation_weight=cfg_tf.saturation.weight,
-            saturation_min_max=cfg_tf.saturation.min_max,
-            hue_weight=cfg_tf.hue.weight,
-            hue_min_max=cfg_tf.hue.min_max,
-            sharpness_weight=cfg_tf.sharpness.weight,
-            sharpness_min_max=cfg_tf.sharpness.min_max,
-            max_num_transforms=cfg_tf.max_num_transforms,
-            random_order=cfg_tf.random_order,
-        )
+def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDataset:
+    """Handles the logic of setting up delta timestamps and image transforms before creating a dataset.
 
-    if isinstance(cfg.dataset_repo_id, str):
-        # TODO (aliberts): add 'episodes' arg from config after removing hydra
+    Args:
+        cfg (TrainPipelineConfig): A TrainPipelineConfig config which contains a DatasetConfig and a PreTrainedConfig.
+
+    Raises:
+        NotImplementedError: The MultiLeRobotDataset is currently deactivated.
+
+    Returns:
+        LeRobotDataset | MultiLeRobotDataset
+    """
+    image_transforms = (
+        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
+    )
+
+    if isinstance(cfg.dataset.repo_id, str):
+        ds_meta = LeRobotDatasetMetadata(cfg.dataset.repo_id, local_files_only=cfg.dataset.local_files_only)
+        delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
         dataset = LeRobotDataset(
-            cfg.dataset_repo_id,
-            delta_timestamps=cfg.training.get("delta_timestamps"),
+            cfg.dataset.repo_id,
+            episodes=cfg.dataset.episodes,
+            delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
-            video_backend=cfg.video_backend,
+            video_backend=cfg.dataset.video_backend,
+            local_files_only=cfg.dataset.local_files_only,
         )
     else:
+        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
         dataset = MultiLeRobotDataset(
-            cfg.dataset_repo_id,
-            delta_timestamps=cfg.training.get("delta_timestamps"),
+            cfg.dataset.repo_id,
+            # TODO(aliberts): add proper support for multi dataset
+            # delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
-            video_backend=cfg.video_backend,
+            video_backend=cfg.dataset.video_backend,
+        )
+        logging.info(
+            "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
+            f"{pformat(dataset.repo_id_to_index , indent=2)}"
         )
 
-    if cfg.get("override_dataset_stats"):
-        for key, stats_dict in cfg.override_dataset_stats.items():
-            for stats_type, listconfig in stats_dict.items():
-                # example of stats_type: min, max, mean, std
-                stats = OmegaConf.to_container(listconfig, resolve=True)
+    if cfg.dataset.use_imagenet_stats:
+        for key in dataset.meta.camera_keys:
+            for stats_type, stats in IMAGENET_STATS.items():
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 
     return dataset

lerobot/common/datasets/lerobot_dataset.py

@@ -840,7 +840,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def stop_image_writer(self) -> None:
         """
         Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to
-        remove the image_write in order for the LeRobotDataset object to be pickleable and parallelized.
+        remove the image_writer in order for the LeRobotDataset object to be pickleable and parallelized.
         """
         if self.image_writer is not None:
             self.image_writer.stop()

lerobot/common/datasets/transforms.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
+from dataclasses import dataclass, field
 from typing import Any, Callable, Sequence
 
 import torch
@@ -65,6 +66,8 @@ class RandomSubsetApply(Transform):
         self.n_subset = n_subset
         self.random_order = random_order
 
+        self.selected_transforms = None
+
     def forward(self, *inputs: Any) -> Any:
         needs_unpacking = len(inputs) > 1
 
@@ -72,9 +75,9 @@ class RandomSubsetApply(Transform):
         if not self.random_order:
             selected_indices = selected_indices.sort().values
 
-        selected_transforms = [self.transforms[i] for i in selected_indices]
+        self.selected_transforms = [self.transforms[i] for i in selected_indices]
 
-        for transform in selected_transforms:
+        for transform in self.selected_transforms:
             outputs = transform(*inputs)
             inputs = outputs if needs_unpacking else (outputs,)
 
@@ -138,61 +141,109 @@ class SharpnessJitter(Transform):
         return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
 
 
-def get_image_transforms(
-    brightness_weight: float = 1.0,
-    brightness_min_max: tuple[float, float] | None = None,
-    contrast_weight: float = 1.0,
-    contrast_min_max: tuple[float, float] | None = None,
-    saturation_weight: float = 1.0,
-    saturation_min_max: tuple[float, float] | None = None,
-    hue_weight: float = 1.0,
-    hue_min_max: tuple[float, float] | None = None,
-    sharpness_weight: float = 1.0,
-    sharpness_min_max: tuple[float, float] | None = None,
-    max_num_transforms: int | None = None,
-    random_order: bool = False,
-):
-    def check_value(name, weight, min_max):
-        if min_max is not None:
-            if len(min_max) != 2:
-                raise ValueError(
-                    f"`{name}_min_max` is expected to be a tuple of 2 dimensions, but {min_max} provided."
-                )
-            if weight < 0.0:
-                raise ValueError(
-                    f"`{name}_weight` is expected to be 0 or positive, but is negative ({weight})."
-                )
+@dataclass
+class ImageTransformConfig:
+    """
+    For each transform, the following parameters are available:
+      weight: This represents the multinomial probability (with no replacement)
+            used for sampling the transform. If the sum of the weights is not 1,
+            they will be normalized.
+      type: The name of the class used. This is either a class available under torchvision.transforms.v2 or a
+            custom transform defined here.
+      kwargs: Lower & upper bound respectively used for sampling the transform's parameter
+            (following uniform distribution) when it's applied.
+    """
 
-    check_value("brightness", brightness_weight, brightness_min_max)
-    check_value("contrast", contrast_weight, contrast_min_max)
-    check_value("saturation", saturation_weight, saturation_min_max)
-    check_value("hue", hue_weight, hue_min_max)
-    check_value("sharpness", sharpness_weight, sharpness_min_max)
+    weight: float = 1.0
+    type: str = "Identity"
+    kwargs: dict[str, Any] = field(default_factory=dict)
 
-    weights = []
-    transforms = []
-    if brightness_min_max is not None and brightness_weight > 0.0:
-        weights.append(brightness_weight)
-        transforms.append(v2.ColorJitter(brightness=brightness_min_max))
-    if contrast_min_max is not None and contrast_weight > 0.0:
-        weights.append(contrast_weight)
-        transforms.append(v2.ColorJitter(contrast=contrast_min_max))
-    if saturation_min_max is not None and saturation_weight > 0.0:
-        weights.append(saturation_weight)
-        transforms.append(v2.ColorJitter(saturation=saturation_min_max))
-    if hue_min_max is not None and hue_weight > 0.0:
-        weights.append(hue_weight)
-        transforms.append(v2.ColorJitter(hue=hue_min_max))
-    if sharpness_min_max is not None and sharpness_weight > 0.0:
-        weights.append(sharpness_weight)
-        transforms.append(SharpnessJitter(sharpness=sharpness_min_max))
 
-    n_subset = len(transforms)
-    if max_num_transforms is not None:
-        n_subset = min(n_subset, max_num_transforms)
+@dataclass
+class ImageTransformsConfig:
+    """
+    These transforms are all using standard torchvision.transforms.v2
+    You can find out how these transformations affect images here:
+    https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html
+    We use a custom RandomSubsetApply container to sample them.
+    """
 
-    if n_subset == 0:
-        return v2.Identity()
+    # Set this flag to `true` to enable transforms during training
+    enable: bool = False
+    # This is the maximum number of transforms (sampled from these below) that will be applied to each frame.
+    # It's an integer in the interval [1, number_of_available_transforms].
+    max_num_transforms: int = 3
+    # By default, transforms are applied in Torchvision's suggested order (shown below).
+    # Set this to True to apply them in a random order.
+    random_order: bool = False
+    tfs: dict[str, ImageTransformConfig] = field(
+        default_factory=lambda: {
+            "brightness": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"brightness": (0.8, 1.2)},
+            ),
+            "contrast": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"contrast": (0.8, 1.2)},
+            ),
+            "saturation": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"saturation": (0.5, 1.5)},
+            ),
+            "hue": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"hue": (-0.05, 0.05)},
+            ),
+            "sharpness": ImageTransformConfig(
+                weight=1.0,
+                type="SharpnessJitter",
+                kwargs={"sharpness": (0.5, 1.5)},
+            ),
+        }
+    )
+
+
+def make_transform_from_config(cfg: ImageTransformConfig):
+    if cfg.type == "Identity":
+        return v2.Identity(**cfg.kwargs)
+    elif cfg.type == "ColorJitter":
+        return v2.ColorJitter(**cfg.kwargs)
+    elif cfg.type == "SharpnessJitter":
+        return SharpnessJitter(**cfg.kwargs)
     else:
-        # TODO(rcadene, aliberts): add v2.ToDtype float16?
-        return RandomSubsetApply(transforms, p=weights, n_subset=n_subset, random_order=random_order)
+        raise ValueError(f"Transform '{cfg.type}' is not valid.")
+
+
+class ImageTransforms(Transform):
+    """A class to compose image transforms based on configuration."""
+
+    def __init__(self, cfg: ImageTransformsConfig) -> None:
+        super().__init__()
+        self._cfg = cfg
+
+        self.weights = []
+        self.transforms = {}
+        for tf_name, tf_cfg in cfg.tfs.items():
+            if tf_cfg.weight <= 0.0:
+                continue
+
+            self.transforms[tf_name] = make_transform_from_config(tf_cfg)
+            self.weights.append(tf_cfg.weight)
+
+        n_subset = min(len(self.transforms), cfg.max_num_transforms)
+        if n_subset == 0 or not cfg.enable:
+            self.tf = v2.Identity()
+        else:
+            self.tf = RandomSubsetApply(
+                transforms=list(self.transforms.values()),
+                p=self.weights,
+                n_subset=n_subset,
+                random_order=cfg.random_order,
+            )
+
+    def forward(self, *inputs: Any) -> Any:
+        return self.tf(*inputs)

lerobot/common/datasets/utils.py

@@ -35,6 +35,7 @@ from PIL import Image as PILImage
 from torchvision import transforms
 
 from lerobot.common.robot_devices.robots.utils import Robot
+from lerobot.configs.types import DictLike, FeatureType, PolicyFeature
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of episodes per chunk
 
@@ -98,6 +99,18 @@ def unflatten_dict(d: dict, sep: str = "/") -> dict:
     return outdict
 
 
+def get_nested_item(obj: DictLike, flattened_key: str, sep: str = "/") -> Any:
+    split_keys = flattened_key.split(sep)
+    getter = obj[split_keys[0]]
+    if len(split_keys) == 1:
+        return getter
+
+    for key in split_keys[1:]:
+        getter = getter[key]
+
+    return getter
+
+
 def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
     serialized_dict = {key: value.tolist() for key, value in flatten_dict(stats).items()}
     return unflatten_dict(serialized_dict)
@@ -289,6 +302,37 @@ def get_features_from_robot(robot: Robot, use_videos: bool = True) -> dict:
     return {**robot.motor_features, **camera_ft, **DEFAULT_FEATURES}
 
 
+def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]:
+    # TODO(aliberts): Implement "type" in dataset features and simplify this
+    policy_features = {}
+    for key, ft in features.items():
+        shape = ft["shape"]
+        if ft["dtype"] in ["image", "video"]:
+            type = FeatureType.VISUAL
+            if len(shape) != 3:
+                raise ValueError(f"Number of dimensions of {key} != 3 (shape={shape})")
+
+            names = ft["names"]
+            # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
+            if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
+                shape = (shape[2], shape[0], shape[1])
+        elif key == "observation.environment_state":
+            type = FeatureType.ENV
+        elif key.startswith("observation"):
+            type = FeatureType.STATE
+        elif key == "action":
+            type = FeatureType.ACTION
+        else:
+            continue
+
+        policy_features[key] = PolicyFeature(
+            type=type,
+            shape=shape,
+        )
+
+    return policy_features
+
+
 def create_empty_dataset_info(
     codebase_version: str,
     fps: int,
@@ -436,7 +480,7 @@ def check_delta_timestamps(
 def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]:
     delta_indices = {}
     for key, delta_ts in delta_timestamps.items():
-        delta_indices[key] = (torch.tensor(delta_ts) * fps).long().tolist()
+        delta_indices[key] = [round(d * fps) for d in delta_ts]
 
     return delta_indices
 

lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py

@@ -26,13 +26,13 @@ from pathlib import Path
 from textwrap import dedent
 
 from lerobot import available_datasets
-from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset, parse_robot_config
+from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
+from lerobot.common.robot_devices.robots.configs import AlohaRobotConfig
 
 LOCAL_DIR = Path("data/")
 
-ALOHA_CONFIG = Path("lerobot/configs/robot/aloha.yaml")
 ALOHA_MOBILE_INFO = {
-    "robot_config": parse_robot_config(ALOHA_CONFIG),
+    "robot_config": AlohaRobotConfig(),
     "license": "mit",
     "url": "https://mobile-aloha.github.io/",
     "paper": "https://arxiv.org/abs/2401.02117",
@@ -45,7 +45,7 @@ ALOHA_MOBILE_INFO = {
         }""").lstrip(),
 }
 ALOHA_STATIC_INFO = {
-    "robot_config": parse_robot_config(ALOHA_CONFIG),
+    "robot_config": AlohaRobotConfig(),
     "license": "mit",
     "url": "https://tonyzhaozh.github.io/aloha/",
     "paper": "https://arxiv.org/abs/2304.13705",

lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py

@@ -141,7 +141,8 @@ from lerobot.common.datasets.video_utils import (
     get_image_pixel_channels,
     get_video_info,
 )
-from lerobot.common.utils.utils import init_hydra_config
+from lerobot.common.robot_devices.robots.configs import RobotConfig
+from lerobot.common.robot_devices.robots.utils import make_robot_config
 
 V16 = "v1.6"
 V20 = "v2.0"
@@ -152,19 +153,18 @@ V1_INFO_PATH = "meta_data/info.json"
 V1_STATS_PATH = "meta_data/stats.safetensors"
 
 
-def parse_robot_config(config_path: Path, config_overrides: list[str] | None = None) -> tuple[str, dict]:
-    robot_cfg = init_hydra_config(config_path, config_overrides)
-    if robot_cfg["robot_type"] in ["aloha", "koch"]:
+def parse_robot_config(robot_cfg: RobotConfig) -> tuple[str, dict]:
+    if robot_cfg.type in ["aloha", "koch"]:
         state_names = [
-            f"{arm}_{motor}" if len(robot_cfg["follower_arms"]) > 1 else motor
-            for arm in robot_cfg["follower_arms"]
-            for motor in robot_cfg["follower_arms"][arm]["motors"]
+            f"{arm}_{motor}" if len(robot_cfg.follower_arms) > 1 else motor
+            for arm in robot_cfg.follower_arms
+            for motor in robot_cfg.follower_arms[arm].motors
         ]
         action_names = [
             # f"{arm}_{motor}" for arm in ["left", "right"] for motor in robot_cfg["leader_arms"][arm]["motors"]
-            f"{arm}_{motor}" if len(robot_cfg["leader_arms"]) > 1 else motor
-            for arm in robot_cfg["leader_arms"]
-            for motor in robot_cfg["leader_arms"][arm]["motors"]
+            f"{arm}_{motor}" if len(robot_cfg.leader_arms) > 1 else motor
+            for arm in robot_cfg.leader_arms
+            for motor in robot_cfg.leader_arms[arm].motors
         ]
     # elif robot_cfg["robot_type"] == "stretch3": TODO
     else:
@@ -173,7 +173,7 @@ def parse_robot_config(config_path: Path, config_overrides: list[str] | None = N
         )
 
     return {
-        "robot_type": robot_cfg["robot_type"],
+        "robot_type": robot_cfg.type,
         "names": {
             "observation.state": state_names,
             "observation.effort": state_names,
@@ -203,7 +203,10 @@ def convert_stats_to_json(v1_dir: Path, v2_dir: Path) -> None:
         torch.testing.assert_close(stats_json[key], stats[key])
 
 
-def get_features_from_hf_dataset(dataset: Dataset, robot_config: dict | None = None) -> dict[str, list]:
+def get_features_from_hf_dataset(
+    dataset: Dataset, robot_config: RobotConfig | None = None
+) -> dict[str, list]:
+    robot_config = parse_robot_config(robot_config)
     features = {}
     for key, ft in dataset.features.items():
         if isinstance(ft, datasets.Value):
@@ -224,11 +227,11 @@ def get_features_from_hf_dataset(dataset: Dataset, robot_config: dict | None = N
             image = dataset[0][key]  # Assuming first row
             channels = get_image_pixel_channels(image)
             shape = (image.height, image.width, channels)
-            names = ["height", "width", "channel"]
+            names = ["height", "width", "channels"]
         elif ft._type == "VideoFrame":
             dtype = "video"
             shape = None  # Add shape later
-            names = ["height", "width", "channel"]
+            names = ["height", "width", "channels"]
 
         features[key] = {
             "dtype": dtype,
@@ -436,7 +439,7 @@ def convert_dataset(
     single_task: str | None = None,
     tasks_path: Path | None = None,
     tasks_col: Path | None = None,
-    robot_config: dict | None = None,
+    robot_config: RobotConfig | None = None,
     test_branch: str | None = None,
     **card_kwargs,
 ):
@@ -532,7 +535,7 @@ def convert_dataset(
     episode_lengths = split_parquet_by_episodes(dataset, total_episodes, total_chunks, v20_dir)
 
     if robot_config is not None:
-        robot_type = robot_config["robot_type"]
+        robot_type = robot_config.type
         repo_tags = [robot_type]
     else:
         robot_type = "unknown"
@@ -621,16 +624,10 @@ def main():
         help="The path to a .json file containing one language instruction for each episode_index",
     )
     parser.add_argument(
-        "--robot-config",
-        type=Path,
-        default=None,
-        help="Path to the robot's config yaml the dataset during conversion.",
-    )
-    parser.add_argument(
-        "--robot-overrides",
+        "--robot",
         type=str,
-        nargs="*",
-        help="Any key=value arguments to override the robot config values (use dots for.nested=overrides)",
+        default=None,
+        help="Robot config used for the dataset during conversion (e.g. 'koch', 'aloha', 'so100', etc.)",
     )
     parser.add_argument(
         "--local-dir",
@@ -655,8 +652,10 @@ def main():
     if not args.local_dir:
         args.local_dir = Path("/tmp/lerobot_dataset_v2")
 
-    robot_config = parse_robot_config(args.robot_config, args.robot_overrides) if args.robot_config else None
-    del args.robot_config, args.robot_overrides
+    if args.robot is not None:
+        robot_config = make_robot_config(args.robot)
+
+    del args.robot
 
     convert_dataset(**vars(args), robot_config=robot_config)