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Author SHA1 Message Date
CarolinePascal 69e6db6925 tests(test): adding new test 2026-06-16 16:43:54 +02:00
CarolinePascal b02e79bb5e fix(features copy): adding deepcopy on LeRobot dataset features to avoid shallow copy leaks 2026-06-16 16:14:18 +02:00
Pepijn 58ccc01508 fix(datasets): enforce one parquet row group per episode in v3 data writes (#3807)
* fix(datasets): enforce one parquet row group per episode in v3 data writes

LeRobot v3 data shards must hold exactly one row group per episode so a
reader can fetch episode i with pq.ParquetFile(path).read_row_group(i)
(a byte-range read) instead of loading the whole shard. The recording
writer already does this (one write_table per episode); the aggregate
and lerobot-annotate re-write paths instead concatenated many episodes
and wrote them in one shot, collapsing the file to a single row group.

- io_utils: add write_table_one_row_group_per_episode (one ParquetWriter,
  one write_table per episode — same pattern as the recording writer);
  to_parquet_with_hf_images embeds images then writes per-episode row
  groups; to_parquet_one_row_group_per_episode wraps it for plain frames
- aggregate: route non-image data writes through the per-episode writer;
  leave the episodes-metadata parquet untouched (already one row/episode)
- annotate: rewrite shards via the per-episode writer instead of a single
  bulk pq.write_table
- tests: invariant coverage through the aggregate (image + video) and
  annotate paths

No change to on-disk schema, paths, naming, rollover thresholds, or
compression. Readers stay backward-compatible (old collapsed files load).

* Update src/lerobot/datasets/io_utils.py

Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* Update src/lerobot/datasets/io_utils.py

Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(datasets): correct indentation and add strict= in row-group helper

The web-edited numpy version of write_table_one_row_group_per_episode had an
over-indented line (IndentationError, breaking pre-commit + test collection)
and a zip() without strict=. Fix both; behaviour unchanged.

---------

Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
2026-06-16 12:15:48 +02:00
Caroline Pascal 38327fdc84 fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion (#3783)
* fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion when videos are not used

* fix(docstrings): improving docstrings

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

---------

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>
2026-06-15 17:55:52 +02:00
Steven Palma 9555efc02c chore(dependencies): update uv.lock (#3595)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
2026-06-15 16:29:44 +02:00
Steven Palma d576c59afb refactor(robots): homogenize bi-manual setups implementations (#3772)
* chore(robots): homogenize bi setups

* feat(robots): split openarm mini into single and bi

* refactor(robots): mixin for bi classes

* docs: update docs
2026-06-15 16:28:54 +02:00
40 changed files with 1633 additions and 1350 deletions
+8 -8
View File
@@ -57,11 +57,11 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with
**Compatible teleoperators:**
- `openarm_mini` - OpenArm Mini
- `bi_openarm_mini` - Bimanual OpenArm Mini
- `so_leader` - SO100 / SO101 leader arm
> [!IMPORTANT]
> The provided commands default to `bi_openarm_follower` + `openarm_mini`.
> The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.
> `so_follower` + `so_leader` configs are also registered and can be used via CLI flags.
---
@@ -104,9 +104,9 @@ lerobot-rollout --strategy.type=dagger \
--robot.right_arm_config.port=can0 \
--robot.right_arm_config.side=right \
--robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
--teleop.type=openarm_mini \
--teleop.port_left=/dev/ttyACM0 \
--teleop.port_right=/dev/ttyACM1 \
--teleop.type=bi_openarm_mini \
--teleop.left_arm_config.port=/dev/ttyACM0 \
--teleop.right_arm_config.port=/dev/ttyACM1 \
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
--dataset.repo_id=your-username/rollout_hil_dataset \
--dataset.single_task="Fold the T-shirt properly" \
@@ -131,9 +131,9 @@ lerobot-rollout --strategy.type=dagger \
--robot.right_arm_config.port=can0 \
--robot.right_arm_config.side=right \
--robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
--teleop.type=openarm_mini \
--teleop.port_left=/dev/ttyACM0 \
--teleop.port_right=/dev/ttyACM1 \
--teleop.type=bi_openarm_mini \
--teleop.left_arm_config.port=/dev/ttyACM0 \
--teleop.right_arm_config.port=/dev/ttyACM1 \
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
--dataset.repo_id=your-username/rollout_hil_rtc_dataset \
--dataset.single_task="Fold the T-shirt properly" \
+1 -1
View File
@@ -117,7 +117,7 @@ lerobot-rollout \
--strategy.num_episodes=20 \
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
--robot.type=bi_openarm_follower \
--teleop.type=openarm_mini \
--teleop.type=bi_openarm_mini \
--dataset.repo_id=${HF_USER}/rollout_hil_data \
--dataset.single_task="Fold the T-shirt"
```
@@ -54,6 +54,7 @@ from typing import Any
import pyarrow as pa
import pyarrow.parquet as pq
from lerobot.datasets.io_utils import write_table_one_row_group_per_episode
from lerobot.datasets.language import (
EVENT_ONLY_STYLES,
LANGUAGE_EVENTS,
@@ -274,12 +275,11 @@ class LanguageColumnsWriter:
new_table = self._materialize_table(
table, per_row_persistent, per_row_events, drop_old=self.drop_existing_subtask_index
)
# Atomic replace: write to a sibling tmp path and rename so a crash
# mid-write can't leave a half-written shard that ``pq.read_table``
# would then fail to open. ``Path.replace`` is atomic on POSIX +
# Windows when source and target sit on the same filesystem.
# Re-emit one row group per episode (a bulk pq.write_table would collapse
# them into one). Write to a sibling tmp path and atomically rename so a
# crash mid-write can't leave a half-written shard.
tmp_path = path.with_suffix(path.suffix + ".tmp")
pq.write_table(new_table, tmp_path)
write_table_one_row_group_per_episode(new_table, tmp_path)
tmp_path.replace(path)
def _materialize_table(
+9
View File
@@ -32,6 +32,7 @@ from .feature_utils import features_equal_for_merge, get_hf_features_from_featur
from .io_utils import (
get_file_size_in_mb,
get_parquet_file_size_in_mb,
to_parquet_one_row_group_per_episode,
to_parquet_with_hf_images,
write_info,
write_stats,
@@ -551,6 +552,7 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
aggr_root=dst_meta.root,
hf_features=hf_features,
concatenate=concatenate_data,
one_row_group_per_episode=True,
)
# Record the mapping from source to actual destination
@@ -628,6 +630,7 @@ def append_or_create_parquet_file(
aggr_root: Path = None,
hf_features: datasets.Features | None = None,
concatenate: bool = True,
one_row_group_per_episode: bool = False,
) -> tuple[dict[str, int], tuple[int, int]]:
"""Appends data to an existing parquet file or creates a new one based on size constraints.
@@ -645,6 +648,8 @@ def append_or_create_parquet_file(
aggr_root: Root path for the aggregated dataset.
hf_features: Optional HuggingFace Features schema for proper image typing.
concatenate: When False, always rotate to a new file instead of appending to the current one.
one_row_group_per_episode: True for DATA parquet (emit one row group per episode); False for
the episodes-metadata parquet (already one row per episode).
Returns:
tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
@@ -657,6 +662,8 @@ def append_or_create_parquet_file(
dst_path.parent.mkdir(parents=True, exist_ok=True)
if contains_images:
to_parquet_with_hf_images(df, dst_path, features=hf_features)
elif one_row_group_per_episode:
to_parquet_one_row_group_per_episode(df, dst_path)
else:
df.to_parquet(dst_path)
return idx, (dst_chunk, dst_file)
@@ -683,6 +690,8 @@ def append_or_create_parquet_file(
if contains_images:
to_parquet_with_hf_images(final_df, target_path, features=hf_features)
elif one_row_group_per_episode:
to_parquet_one_row_group_per_episode(final_df, target_path)
else:
final_df.to_parquet(target_path)
+2 -1
View File
@@ -15,6 +15,7 @@
# limitations under the License.
import contextlib
from collections.abc import Callable
from copy import deepcopy
from pathlib import Path
import numpy as np
@@ -709,7 +710,7 @@ class LeRobotDatasetMetadata:
obj.root.mkdir(parents=True, exist_ok=False)
features = {**features, **DEFAULT_FEATURES}
features = {**deepcopy(features), **DEFAULT_FEATURES}
_validate_feature_names(features)
obj.tasks = None
+4 -1
View File
@@ -27,6 +27,7 @@ import logging
import shutil
from collections.abc import Callable
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
from copy import deepcopy
from pathlib import Path
import datasets
@@ -1101,7 +1102,9 @@ def _copy_episodes_metadata_and_stats(
if dst_meta.video_keys and src_dataset.meta.video_keys:
for key in dst_meta.video_keys:
if key in src_dataset.meta.features:
dst_meta.info.features[key]["info"] = src_dataset.meta.info.features[key].get("info", {})
dst_meta.info.features[key]["info"] = deepcopy(
src_dataset.meta.info.features[key].get("info", {})
)
write_info(dst_meta.info, dst_meta.root)
+38 -9
View File
@@ -20,6 +20,7 @@ import datasets
import numpy as np
import pandas
import pandas as pd
import pyarrow as pa
import pyarrow.dataset as pa_ds
import pyarrow.parquet as pq
import torch
@@ -270,21 +271,49 @@ def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[to
return items_dict
def write_table_one_row_group_per_episode(table: pa.Table, path: Path) -> None:
"""Write ``table`` with one parquet row group per episode (in episode order).
Keeps shards random-access friendly (``read_row_group(i)`` fetches episode i),
mirroring the recording writer. ``table`` must carry a contiguous
``episode_index`` column.
"""
episode_index = table.column("episode_index").to_numpy(zero_copy_only=False)
starts = np.concatenate(([0], np.nonzero(np.diff(episode_index))[0] + 1))
writer = pq.ParquetWriter(str(path), table.schema, compression="snappy", use_dictionary=True)
try:
for start, stop in zip(starts, np.append(starts[1:], len(episode_index)), strict=True):
writer.write_table(table.slice(start, stop - start)) # one episode -> one row group
finally:
writer.close()
def to_parquet_with_hf_images(
df: pandas.DataFrame, path: Path, features: datasets.Features | None = None
) -> None:
"""This function correctly writes to parquet a panda DataFrame that contains images encoded by HF dataset.
This way, it can be loaded by HF dataset and correctly formatted images are returned.
"""Write a DataFrame with HF-encoded images to parquet, one row group per episode.
Args:
df: DataFrame to write to parquet.
path: Path to write the parquet file.
features: Optional HuggingFace Features schema. If provided, ensures image columns
are properly typed as Image() in the parquet schema.
Images are embedded into the arrow table first (``ParquetWriter.write_table``
does not embed external image files like ``Dataset.to_parquet`` does).
``features`` types image columns as ``Image()`` in the parquet schema.
"""
# TODO(qlhoest): replace this weird synthax by `df.to_parquet(path)` only
ds = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=features)
ds.to_parquet(path)
ds = embed_images(ds)
table = ds.with_format("arrow")[:]
if "episode_index" in table.column_names:
write_table_one_row_group_per_episode(table, path)
else:
# No episode boundaries to align row groups to — keep a single write.
pq.write_table(table, str(path))
def to_parquet_one_row_group_per_episode(df: pandas.DataFrame, path: Path) -> None:
"""Write a (non-image) DataFrame to parquet with one row group per episode."""
table = pa.Table.from_pandas(df, preserve_index=False)
if "episode_index" in table.column_names:
write_table_one_row_group_per_episode(table, path)
else:
pq.write_table(table, str(path))
def item_to_torch(item: dict) -> dict:
+5 -3
View File
@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
initial_features: dict[PipelineFeatureType, dict[str, Any]],
*,
use_videos: bool = True,
exclude_images: bool = False,
patterns: Sequence[str] | None = None,
) -> dict[str, dict]:
"""
Aggregates and filters pipeline features to create a dataset-ready features dictionary.
This function transforms initial features using the pipeline, categorizes them as action or observations
(image or state), filters them based on `use_videos` and `patterns`, and finally
(image or state), filters them based on `exclude_images` and `patterns`, and finally
formats them for use with a Hugging Face LeRobot Dataset.
Args:
pipeline: The DataProcessorPipeline to apply.
initial_features: A dictionary of raw feature specs for actions and observations.
use_videos: If False, image features are excluded.
use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
exclude_images: If True, image features are dropped entirely from the output.
patterns: A sequence of regex patterns to filter action and state features.
Image features are not affected by this filter.
@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
)
# 2. Apply filtering rules.
if is_image and not use_videos:
if is_image and exclude_images:
continue
if not is_image and not should_keep(key, compiled_patterns):
continue
@@ -18,7 +18,8 @@ import logging
from functools import cached_property
from lerobot.types import RobotAction, RobotObservation
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..openarm_follower import OpenArmFollower, OpenArmFollowerConfig
from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_openarm_follower import BiOpenArmFollowerConfig
logger = logging.getLogger(__name__)
class BiOpenArmFollower(Robot):
class BiOpenArmFollower(BimanualMixin, Robot):
"""
Bimanual OpenArm Follower Arms
"""
@@ -39,15 +40,17 @@ class BiOpenArmFollower(Robot):
super().__init__(config)
self.config = config
# Top-level cameras are distributed evenly: each arm's OpenArmFollower
# will only open the cameras assigned to it. Per-arm cameras are used
# as fallback when top-level cameras are empty.
if config.cameras:
left_cameras = config.cameras
right_cameras = {}
else:
left_cameras = config.left_arm_config.cameras
right_cameras = config.right_arm_config.cameras
# Top-level cameras are opened by `left_arm` for convenience, but their
# keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
self._top_level_cam_keys = set(config.cameras)
_collisions = self._top_level_cam_keys & set(
config.left_arm_config.cameras
) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
if _collisions:
raise ValueError(
f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
)
left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
left_arm_config = OpenArmFollowerConfig(
id=f"{config.id}_left" if config.id else None,
@@ -56,7 +59,7 @@ class BiOpenArmFollower(Robot):
disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
use_velocity_and_torque=config.left_arm_config.use_velocity_and_torque,
max_relative_target=config.left_arm_config.max_relative_target,
cameras=left_cameras,
cameras=left_arm_cameras,
side=config.left_arm_config.side,
can_interface=config.left_arm_config.can_interface,
use_can_fd=config.left_arm_config.use_can_fd,
@@ -75,7 +78,7 @@ class BiOpenArmFollower(Robot):
disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
use_velocity_and_torque=config.right_arm_config.use_velocity_and_torque,
max_relative_target=config.right_arm_config.max_relative_target,
cameras=right_cameras,
cameras=config.right_arm_config.cameras,
side=config.right_arm_config.side,
can_interface=config.right_arm_config.can_interface,
use_can_fd=config.right_arm_config.use_can_fd,
@@ -95,22 +98,19 @@ class BiOpenArmFollower(Robot):
@property
def _motors_ft(self) -> dict[str, type]:
left_arm_motors_ft = self.left_arm._motors_ft
right_arm_motors_ft = self.right_arm._motors_ft
# Right first, then left — matches the teleoperator (OpenArmMini) ordering
# and the dataset feature names recorded during data collection.
return {
**{f"right_{k}": v for k, v in right_arm_motors_ft.items()},
**{f"left_{k}": v for k, v in left_arm_motors_ft.items()},
**{f"left_{k}": v for k, v in self.left_arm._motors_ft.items()},
**{f"right_{k}": v for k, v in self.right_arm._motors_ft.items()},
}
@property
def _cameras_ft(self) -> dict[str, tuple]:
# Cameras already have unique user-chosen names (e.g. "left_wrist", "base",
# "right_wrist"), so we merge them directly — unlike motors which need the
# left_/right_ prefix to disambiguate identical per-arm joint names.
return {**self.left_arm._cameras_ft, **self.right_arm._cameras_ft}
out: dict[str, tuple] = {}
for k, v in self.left_arm._cameras_ft.items():
out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
for k, v in self.right_arm._cameras_ft.items():
out[f"right_{k}"] = v
return out
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
@@ -120,27 +120,6 @@ class BiOpenArmFollower(Robot):
def action_features(self) -> dict[str, type]:
return self._motors_ft
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
def setup_motors(self) -> None:
raise NotImplementedError(
"Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -148,21 +127,15 @@ class BiOpenArmFollower(Robot):
@check_if_not_connected
def get_observation(self) -> RobotObservation:
obs_dict = {}
obs_dict: RobotObservation = {}
# Camera keys that should NOT get the arm prefix (they already have unique names)
left_cam_keys = set(self.left_arm.cameras.keys())
right_cam_keys = set(self.right_arm.cameras.keys())
# Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
for key, value in self.left_arm.get_observation().items():
obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
# Right first, then left — matches the teleoperator (OpenArmMini) ordering
# and the dataset feature names recorded during data collection.
right_obs = self.right_arm.get_observation()
for key, value in right_obs.items():
obs_dict[key if key in right_cam_keys else f"right_{key}"] = value
left_obs = self.left_arm.get_observation()
for key, value in left_obs.items():
obs_dict[key if key in left_cam_keys else f"left_{key}"] = value
# Add "right_" prefix
for key, value in self.right_arm.get_observation().items():
obs_dict[f"right_{key}"] = value
return obs_dict
@@ -189,9 +162,4 @@ class BiOpenArmFollower(Robot):
prefixed_sent_action_left = {f"left_{key}": value for key, value in sent_action_left.items()}
prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
return {**prefixed_sent_action_right, **prefixed_sent_action_left}
@check_if_not_connected
def disconnect(self):
self.left_arm.disconnect()
self.right_arm.disconnect()
return {**prefixed_sent_action_left, **prefixed_sent_action_right}
@@ -32,5 +32,7 @@ class BiOpenArmFollowerConfig(RobotConfig):
left_arm_config: OpenArmFollowerConfigBase
right_arm_config: OpenArmFollowerConfigBase
# Top-level cameras shared across both arms.
# Top-level cameras not attached to a specific side. Keys are kept as-is in
# observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
# `{left,right}_arm_config.cameras`) are prefixed.
cameras: dict[str, CameraConfig] = field(default_factory=dict)
@@ -18,7 +18,8 @@ import logging
from functools import cached_property
from lerobot.types import RobotAction, RobotObservation
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..rebot_b601_follower import RebotB601Follower, RebotB601FollowerRobotConfig
from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_rebot_b601_follower import BiRebotB601FollowerConfig
logger = logging.getLogger(__name__)
class BiRebotB601Follower(Robot):
class BiRebotB601Follower(BimanualMixin, Robot):
"""Bimanual Seeed Studio reBot B601-DM follower.
Composes two single-arm :class:`RebotB601Follower` instances. Observation and
@@ -41,6 +42,18 @@ class BiRebotB601Follower(Robot):
super().__init__(config)
self.config = config
# Top-level cameras are opened by `left_arm` for convenience, but their
# keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
self._top_level_cam_keys = set(config.cameras)
_collisions = self._top_level_cam_keys & set(
config.left_arm_config.cameras
) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
if _collisions:
raise ValueError(
f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
)
left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
left_arm_config = RebotB601FollowerRobotConfig(
id=f"{config.id}_left" if config.id else None,
calibration_dir=config.calibration_dir,
@@ -49,7 +62,7 @@ class BiRebotB601Follower(Robot):
dm_serial_baud=config.left_arm_config.dm_serial_baud,
disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
max_relative_target=config.left_arm_config.max_relative_target,
cameras=config.left_arm_config.cameras,
cameras=left_arm_cameras,
motor_can_ids=config.left_arm_config.motor_can_ids,
pos_vel_velocity=config.left_arm_config.pos_vel_velocity,
gripper_torque_ratio=config.left_arm_config.gripper_torque_ratio,
@@ -86,10 +99,12 @@ class BiRebotB601Follower(Robot):
@property
def _cameras_ft(self) -> dict[str, tuple]:
return {
**{f"left_{k}": v for k, v in self.left_arm._cameras_ft.items()},
**{f"right_{k}": v for k, v in self.right_arm._cameras_ft.items()},
}
out: dict[str, tuple] = {}
for k, v in self.left_arm._cameras_ft.items():
out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
for k, v in self.right_arm._cameras_ft.items():
out[f"right_{k}"] = v
return out
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
@@ -99,32 +114,13 @@ class BiRebotB601Follower(Robot):
def action_features(self) -> dict[str, type]:
return self._motors_ft
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
@check_if_not_connected
def get_observation(self) -> RobotObservation:
obs_dict = {}
obs_dict.update({f"left_{k}": v for k, v in self.left_arm.get_observation().items()})
obs_dict.update({f"right_{k}": v for k, v in self.right_arm.get_observation().items()})
obs_dict: RobotObservation = {}
for k, v in self.left_arm.get_observation().items():
obs_dict[k if k in self._top_level_cam_keys else f"left_{k}"] = v
for k, v in self.right_arm.get_observation().items():
obs_dict[f"right_{k}"] = v
return obs_dict
@check_if_not_connected
@@ -143,8 +139,3 @@ class BiRebotB601Follower(Robot):
**{f"left_{k}": v for k, v in sent_action_left.items()},
**{f"right_{k}": v for k, v in sent_action_right.items()},
}
@check_if_not_connected
def disconnect(self) -> None:
self.left_arm.disconnect()
self.right_arm.disconnect()
@@ -14,7 +14,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from dataclasses import dataclass, field
from lerobot.cameras import CameraConfig
from ..config import RobotConfig
from ..rebot_b601_follower import RebotB601FollowerConfig
@@ -27,3 +29,8 @@ class BiRebotB601FollowerConfig(RobotConfig):
left_arm_config: RebotB601FollowerConfig
right_arm_config: RebotB601FollowerConfig
# Top-level cameras not attached to a specific side. Keys are kept as-is in
# observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
# `{left,right}_arm_config.cameras`) are prefixed.
cameras: dict[str, CameraConfig] = field(default_factory=dict)
@@ -18,7 +18,8 @@ import logging
from functools import cached_property
from lerobot.types import RobotAction, RobotObservation
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..robot import Robot
from ..so_follower import SOFollower, SOFollowerRobotConfig
@@ -27,7 +28,7 @@ from .config_bi_so_follower import BiSOFollowerConfig
logger = logging.getLogger(__name__)
class BiSOFollower(Robot):
class BiSOFollower(BimanualMixin, Robot):
"""
[Bimanual SO Follower Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
"""
@@ -39,6 +40,18 @@ class BiSOFollower(Robot):
super().__init__(config)
self.config = config
# Top-level cameras are opened by `left_arm` for convenience, but their
# keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
self._top_level_cam_keys = set(config.cameras)
_collisions = self._top_level_cam_keys & set(
config.left_arm_config.cameras
) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
if _collisions:
raise ValueError(
f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
)
left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
left_arm_config = SOFollowerRobotConfig(
id=f"{config.id}_left" if config.id else None,
calibration_dir=config.calibration_dir,
@@ -46,7 +59,7 @@ class BiSOFollower(Robot):
disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
max_relative_target=config.left_arm_config.max_relative_target,
use_degrees=config.left_arm_config.use_degrees,
cameras=config.left_arm_config.cameras,
cameras=left_arm_cameras,
)
right_arm_config = SOFollowerRobotConfig(
@@ -77,13 +90,12 @@ class BiSOFollower(Robot):
@property
def _cameras_ft(self) -> dict[str, tuple]:
left_arm_cameras_ft = self.left_arm._cameras_ft
right_arm_cameras_ft = self.right_arm._cameras_ft
return {
**{f"left_{k}": v for k, v in left_arm_cameras_ft.items()},
**{f"right_{k}": v for k, v in right_arm_cameras_ft.items()},
}
out: dict[str, tuple] = {}
for k, v in self.left_arm._cameras_ft.items():
out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
for k, v in self.right_arm._cameras_ft.items():
out[f"right_{k}"] = v
return out
@cached_property
def observation_features(self) -> dict[str, type | tuple]:
@@ -93,42 +105,21 @@ class BiSOFollower(Robot):
def action_features(self) -> dict[str, type]:
return self._motors_ft
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
def setup_motors(self) -> None:
self.left_arm.setup_motors()
self.right_arm.setup_motors()
@check_if_not_connected
def get_observation(self) -> RobotObservation:
obs_dict = {}
obs_dict: RobotObservation = {}
# Add "left_" prefix
left_obs = self.left_arm.get_observation()
obs_dict.update({f"left_{key}": value for key, value in left_obs.items()})
# Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
for key, value in self.left_arm.get_observation().items():
obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
# Add "right_" prefix
right_obs = self.right_arm.get_observation()
obs_dict.update({f"right_{key}": value for key, value in right_obs.items()})
for key, value in self.right_arm.get_observation().items():
obs_dict[f"right_{key}"] = value
return obs_dict
@@ -151,8 +142,3 @@ class BiSOFollower(Robot):
prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
return {**prefixed_sent_action_left, **prefixed_sent_action_right}
@check_if_not_connected
def disconnect(self):
self.left_arm.disconnect()
self.right_arm.disconnect()
@@ -14,7 +14,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from dataclasses import dataclass, field
from lerobot.cameras import CameraConfig
from ..config import RobotConfig
from ..so_follower import SOFollowerConfig
@@ -27,3 +29,8 @@ class BiSOFollowerConfig(RobotConfig):
left_arm_config: SOFollowerConfig
right_arm_config: SOFollowerConfig
# Top-level cameras not attached to a specific side. Keys are kept as-is in
# observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
# `{left,right}_arm_config.cameras`) are prefixed.
cameras: dict[str, CameraConfig] = field(default_factory=dict)
+1
View File
@@ -54,6 +54,7 @@ from lerobot.teleoperators import ( # noqa: F401
Teleoperator,
TeleoperatorConfig,
bi_openarm_leader,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
homunculus,
@@ -57,6 +57,7 @@ from lerobot.robots import ( # noqa: F401
from lerobot.teleoperators import ( # noqa: F401
TeleoperatorConfig,
bi_openarm_leader,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
gamepad,
+1
View File
@@ -137,6 +137,7 @@ from lerobot.teleoperators import ( # noqa: F401
Teleoperator,
TeleoperatorConfig,
bi_openarm_leader,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
homunculus,
+1
View File
@@ -174,6 +174,7 @@ from lerobot.teleoperators import ( # noqa: F401
Teleoperator,
TeleoperatorConfig,
bi_openarm_leader,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
homunculus,
@@ -41,6 +41,7 @@ from lerobot.robots import ( # noqa: F401
)
from lerobot.teleoperators import ( # noqa: F401
TeleoperatorConfig,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
koch_leader,
@@ -89,6 +89,7 @@ from lerobot.teleoperators import ( # noqa: F401
Teleoperator,
TeleoperatorConfig,
bi_openarm_leader,
bi_openarm_mini,
bi_rebot_102_leader,
bi_so_leader,
gamepad,
@@ -18,7 +18,8 @@ import logging
from functools import cached_property
from lerobot.types import RobotAction
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..openarm_leader import OpenArmLeader, OpenArmLeaderConfig
from ..teleoperator import Teleoperator
@@ -27,7 +28,7 @@ from .config_bi_openarm_leader import BiOpenArmLeaderConfig
logger = logging.getLogger(__name__)
class BiOpenArmLeader(Teleoperator):
class BiOpenArmLeader(BimanualMixin, Teleoperator):
"""
Bimanual OpenArm Leader Arms
"""
@@ -86,27 +87,6 @@ class BiOpenArmLeader(Teleoperator):
def feedback_features(self) -> dict[str, type]:
return {}
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
def setup_motors(self) -> None:
raise NotImplementedError(
"Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -129,8 +109,3 @@ class BiOpenArmLeader(Teleoperator):
def send_feedback(self, feedback: dict[str, float]) -> None:
# TODO: Implement force feedback
raise NotImplementedError
@check_if_not_connected
def disconnect(self) -> None:
self.left_arm.disconnect()
self.right_arm.disconnect()
@@ -23,7 +23,7 @@ from ..openarm_leader import OpenArmLeaderConfigBase
@TeleoperatorConfig.register_subclass("bi_openarm_leader")
@dataclass
class BiOpenArmLeaderConfig(TeleoperatorConfig):
"""Configuration class for Bi OpenArm Follower robots."""
"""Configuration class for Bi OpenArm Leader teleoperators."""
left_arm_config: OpenArmLeaderConfigBase
right_arm_config: OpenArmLeaderConfigBase
@@ -0,0 +1,20 @@
#!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .bi_openarm_mini import BiOpenArmMini
from .config_bi_openarm_mini import BiOpenArmMiniConfig
__all__ = ["BiOpenArmMini", "BiOpenArmMiniConfig"]
@@ -0,0 +1,101 @@
#!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from functools import cached_property
from lerobot.types import RobotAction
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..openarm_mini import OpenArmMini, OpenArmMiniConfig
from ..teleoperator import Teleoperator
from .config_bi_openarm_mini import BiOpenArmMiniConfig
logger = logging.getLogger(__name__)
class BiOpenArmMini(BimanualMixin, Teleoperator):
"""Bimanual OpenArm Mini teleoperator.
Composes two single-arm :class:`OpenArmMini` instances. Action and feedback
keys of each arm are namespaced with a ``left_`` / ``right_`` prefix, so a
bimanual leader can teleoperate a bimanual OpenArm follower.
"""
config_class = BiOpenArmMiniConfig
name = "bi_openarm_mini"
def __init__(self, config: BiOpenArmMiniConfig):
super().__init__(config)
self.config = config
# `side` is forced to match left/right regardless of what the user passed
# on the per-arm base config — the bimanual wrapper owns the side semantics.
left_arm_config = OpenArmMiniConfig(
id=f"{config.id}_left" if config.id else None,
calibration_dir=config.calibration_dir,
port=config.left_arm_config.port,
side="left",
use_degrees=config.left_arm_config.use_degrees,
)
right_arm_config = OpenArmMiniConfig(
id=f"{config.id}_right" if config.id else None,
calibration_dir=config.calibration_dir,
port=config.right_arm_config.port,
side="right",
use_degrees=config.right_arm_config.use_degrees,
)
self.left_arm = OpenArmMini(left_arm_config)
self.right_arm = OpenArmMini(right_arm_config)
@cached_property
def action_features(self) -> dict[str, type]:
return {
**{f"left_{k}": v for k, v in self.left_arm.action_features.items()},
**{f"right_{k}": v for k, v in self.right_arm.action_features.items()},
}
@cached_property
def feedback_features(self) -> dict[str, type]:
return {
**{f"left_{k}": v for k, v in self.left_arm.feedback_features.items()},
**{f"right_{k}": v for k, v in self.right_arm.feedback_features.items()},
}
def setup_motors(self) -> None:
self.left_arm.setup_motors()
self.right_arm.setup_motors()
@check_if_not_connected
def get_action(self) -> RobotAction:
action: RobotAction = {}
for k, v in self.left_arm.get_action().items():
action[f"left_{k}"] = v
for k, v in self.right_arm.get_action().items():
action[f"right_{k}"] = v
return action
@check_if_not_connected
def send_feedback(self, feedback: dict[str, float]) -> None:
left_fb = {k.removeprefix("left_"): v for k, v in feedback.items() if k.startswith("left_")}
right_fb = {k.removeprefix("right_"): v for k, v in feedback.items() if k.startswith("right_")}
if left_fb:
self.left_arm.send_feedback(left_fb)
if right_fb:
self.right_arm.send_feedback(right_fb)
@@ -0,0 +1,29 @@
#!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from ..config import TeleoperatorConfig
from ..openarm_mini import OpenArmMiniConfigBase
@TeleoperatorConfig.register_subclass("bi_openarm_mini")
@dataclass
class BiOpenArmMiniConfig(TeleoperatorConfig):
"""Configuration class for Bi OpenArm Mini teleoperators."""
left_arm_config: OpenArmMiniConfigBase
right_arm_config: OpenArmMiniConfigBase
@@ -14,7 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .bi_rebot_102_leader import BiRebotArm102Leader
from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
from .bi_rebot_102_leader import BiRebot102Leader
from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
__all__ = ["BiRebotArm102Leader", "BiRebotArm102LeaderConfig"]
__all__ = ["BiRebot102Leader", "BiRebot102LeaderConfig"]
@@ -18,16 +18,17 @@ import logging
from functools import cached_property
from lerobot.types import RobotAction
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..rebot_102_leader import RebotArm102Leader, RebotArm102LeaderTeleopConfig
from ..teleoperator import Teleoperator
from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
logger = logging.getLogger(__name__)
class BiRebotArm102Leader(Teleoperator):
class BiRebot102Leader(BimanualMixin, Teleoperator):
"""Bimanual Seeed Studio StarArm102 / reBot Arm 102 leader.
Composes two single-arm :class:`RebotArm102Leader` instances. Action keys of
@@ -35,10 +36,10 @@ class BiRebotArm102Leader(Teleoperator):
leader can teleoperate a bimanual reBot B601 follower.
"""
config_class = BiRebotArm102LeaderConfig
config_class = BiRebot102LeaderConfig
name = "bi_rebot_102_leader"
def __init__(self, config: BiRebotArm102LeaderConfig):
def __init__(self, config: BiRebot102LeaderConfig):
super().__init__(config)
self.config = config
@@ -76,27 +77,6 @@ class BiRebotArm102Leader(Teleoperator):
def feedback_features(self) -> dict[str, type]:
return {}
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
@check_if_not_connected
def get_action(self) -> RobotAction:
action_dict = {}
@@ -106,8 +86,3 @@ class BiRebotArm102Leader(Teleoperator):
def send_feedback(self, feedback: dict[str, float]) -> None:
raise NotImplementedError("Feedback is not implemented for the reBot Arm 102 leader.")
@check_if_not_connected
def disconnect(self) -> None:
self.left_arm.disconnect()
self.right_arm.disconnect()
@@ -22,7 +22,7 @@ from ..rebot_102_leader import RebotArm102LeaderConfig
@TeleoperatorConfig.register_subclass("bi_rebot_102_leader")
@dataclass
class BiRebotArm102LeaderConfig(TeleoperatorConfig):
class BiRebot102LeaderConfig(TeleoperatorConfig):
"""Configuration class for the bimanual reBot Arm 102 leader teleoperator."""
left_arm_config: RebotArm102LeaderConfig
@@ -17,7 +17,9 @@
import logging
from functools import cached_property
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
from lerobot.types import RobotAction
from lerobot.utils.bimanual import BimanualMixin
from lerobot.utils.decorators import check_if_not_connected
from ..so_leader import SOLeader, SOLeaderTeleopConfig
from ..teleoperator import Teleoperator
@@ -26,7 +28,7 @@ from .config_bi_so_leader import BiSOLeaderConfig
logger = logging.getLogger(__name__)
class BiSOLeader(Teleoperator):
class BiSOLeader(BimanualMixin, Teleoperator):
"""
[Bimanual SO Leader Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
"""
@@ -67,33 +69,12 @@ class BiSOLeader(Teleoperator):
def feedback_features(self) -> dict[str, type]:
return {}
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
def setup_motors(self) -> None:
self.left_arm.setup_motors()
self.right_arm.setup_motors()
@check_if_not_connected
def get_action(self) -> dict[str, float]:
def get_action(self) -> RobotAction:
action_dict = {}
# Add "left_" prefix
@@ -109,8 +90,3 @@ class BiSOLeader(Teleoperator):
def send_feedback(self, feedback: dict[str, float]) -> None:
# TODO: Implement force feedback
raise NotImplementedError
@check_if_not_connected
def disconnect(self) -> None:
self.left_arm.disconnect()
self.right_arm.disconnect()
@@ -1,6 +1,6 @@
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,7 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .config_openarm_mini import OpenArmMiniConfig
from .config_openarm_mini import OpenArmMiniConfig, OpenArmMiniConfigBase
from .openarm_mini import OpenArmMini
__all__ = ["OpenArmMini", "OpenArmMiniConfig"]
__all__ = ["OpenArmMini", "OpenArmMiniConfig", "OpenArmMiniConfigBase"]
@@ -19,12 +19,21 @@ from dataclasses import dataclass
from ..config import TeleoperatorConfig
@TeleoperatorConfig.register_subclass("openarm_mini")
@dataclass
class OpenArmMiniConfig(TeleoperatorConfig):
"""Configuration for OpenArm Mini teleoperator with Feetech motors (dual arms)."""
class OpenArmMiniConfigBase:
"""Base configuration for the OpenArm Mini teleoperator (Feetech STS3215, 7DOF + gripper)."""
port_right: str = "/dev/ttyUSB0"
port_left: str = "/dev/ttyUSB1"
# Serial port for the Feetech bus (e.g., "/dev/ttyUSB0").
port: str
# Side of the arm: "left" or "right". Controls per-joint direction flips applied
# during readout. If `None`, no flipping is applied.
side: str | None = None
use_degrees: bool = True
@TeleoperatorConfig.register_subclass("openarm_mini")
@dataclass
class OpenArmMiniConfig(TeleoperatorConfig, OpenArmMiniConfigBase):
pass
@@ -31,22 +31,22 @@ from .config_openarm_mini import OpenArmMiniConfig
logger = logging.getLogger(__name__)
# Motors whose direction is inverted during readout
RIGHT_MOTORS_TO_FLIP = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
LEFT_MOTORS_TO_FLIP = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
# Per-side motor direction flips applied during readout.
SIDE_MOTORS_TO_FLIP: dict[str, list[str]] = {
"left": ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"],
"right": ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"],
}
# Leader joint 6 maps to follower joint 7 and vice versa
# Leader joint 6 follower joint 7 (symmetric — its own inverse).
JOINT_REMAP = {"joint_6": "joint_7", "joint_7": "joint_6"}
JOINT_REMAP_REVERSE = {"joint_7": "joint_6", "joint_6": "joint_7"}
GRIPPER_TELEOP_TO_DEGREES = -0.65
class OpenArmMini(Teleoperator):
"""
OpenArm Mini Teleoperator with dual Feetech-based arms (8 motors per arm).
"""OpenArm Mini single-arm teleoperator (Feetech STS3215, 7DOF + gripper).
Each arm has 7 joints plus a gripper, using Feetech STS3215 servos.
For the bimanual setup, see :class:`BiOpenArmMini` which composes two of these.
"""
config_class = OpenArmMiniConfig
@@ -56,9 +56,12 @@ class OpenArmMini(Teleoperator):
super().__init__(config)
self.config = config
if config.side is not None and config.side not in SIDE_MOTORS_TO_FLIP:
raise ValueError(f"Invalid side '{config.side}'; expected 'left', 'right', or None.")
self._motors_to_flip: list[str] = SIDE_MOTORS_TO_FLIP.get(config.side, []) if config.side else []
norm_mode_body = MotorNormMode.DEGREES
motors_right = {
motors = {
"joint_1": Motor(1, "sts3215", norm_mode_body),
"joint_2": Motor(2, "sts3215", norm_mode_body),
"joint_3": Motor(3, "sts3215", norm_mode_body),
@@ -69,46 +72,15 @@ class OpenArmMini(Teleoperator):
"gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
}
motors_left = {
"joint_1": Motor(1, "sts3215", norm_mode_body),
"joint_2": Motor(2, "sts3215", norm_mode_body),
"joint_3": Motor(3, "sts3215", norm_mode_body),
"joint_4": Motor(4, "sts3215", norm_mode_body),
"joint_5": Motor(5, "sts3215", norm_mode_body),
"joint_6": Motor(6, "sts3215", norm_mode_body),
"joint_7": Motor(7, "sts3215", norm_mode_body),
"gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
}
cal_right = {
k.replace("right_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("right_")
}
cal_left = {
k.replace("left_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("left_")
}
self.bus_right = FeetechMotorsBus(
port=self.config.port_right,
motors=motors_right,
calibration=cal_right,
)
self.bus_left = FeetechMotorsBus(
port=self.config.port_left,
motors=motors_left,
calibration=cal_left,
self.bus = FeetechMotorsBus(
port=self.config.port,
motors=motors,
calibration=self.calibration,
)
@property
def action_features(self) -> dict[str, type]:
# Right first, then left — matches the robot (BiOpenArmFollower) ordering
# and the dataset feature names recorded during data collection.
features: dict[str, type] = {}
for motor in self.bus_right.motors:
features[f"right_{motor}.pos"] = float
for motor in self.bus_left.motors:
features[f"left_{motor}.pos"] = float
return features
return {f"{motor}.pos": float for motor in self.bus.motors}
@property
def feedback_features(self) -> dict[str, type]:
@@ -116,14 +88,12 @@ class OpenArmMini(Teleoperator):
@property
def is_connected(self) -> bool:
return self.bus_right.is_connected and self.bus_left.is_connected
return self.bus.is_connected
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
logger.info(f"Connecting right arm on {self.config.port_right}...")
self.bus_right.connect()
logger.info(f"Connecting left arm on {self.config.port_left}...")
self.bus_left.connect()
logger.info(f"Connecting arm on {self.config.port}...")
self.bus.connect()
if calibrate:
self.calibrate()
@@ -133,14 +103,14 @@ class OpenArmMini(Teleoperator):
@property
def is_calibrated(self) -> bool:
return self.bus_right.is_calibrated and self.bus_left.is_calibrated
return self.bus.is_calibrated
def calibrate(self) -> None:
"""
Run calibration procedure for OpenArm Mini.
Run calibration procedure for a single OpenArm Mini arm.
1. Disable torque
2. Ask user to position arms in hanging position with grippers closed
2. Ask user to position arm in hanging position with gripper closed
3. Set this as zero position via half-turn homing
4. Interactive gripper calibration (open/close positions)
5. Save calibration
@@ -152,70 +122,51 @@ class OpenArmMini(Teleoperator):
)
if user_input.strip().lower() != "c":
logger.info(f"Using existing calibration for {self.id}")
cal_right = {
k.replace("right_", ""): v for k, v in self.calibration.items() if k.startswith("right_")
}
cal_left = {
k.replace("left_", ""): v for k, v in self.calibration.items() if k.startswith("left_")
}
self.bus_right.write_calibration(cal_right)
self.bus_left.write_calibration(cal_left)
self.bus.write_calibration(self.calibration)
return
logger.info(f"\nRunning calibration for {self}")
self._calibrate_arm("right", self.bus_right)
self._calibrate_arm("left", self.bus_left)
self.bus.disable_torque()
self._save_calibration()
print(f"\nCalibration complete and saved to {self.calibration_fpath}")
logger.info("Setting Phase to 12 for all motors...")
for motor in self.bus.motors:
self.bus.write("Phase", motor, 12)
def _calibrate_arm(self, arm_name: str, bus: FeetechMotorsBus) -> None:
"""Calibrate a single arm with Feetech motors."""
logger.info(f"\n=== Calibrating {arm_name.upper()} arm ===")
bus.disable_torque()
logger.info(f"Setting Phase to 12 for all motors in {arm_name.upper()} arm...")
for motor in bus.motors:
bus.write("Phase", motor, 12)
for motor in bus.motors:
bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
for motor in self.bus.motors:
self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
input(
f"\nCalibration: Zero Position ({arm_name.upper()} arm)\n"
"\nCalibration: Zero Position\n"
"Position the arm in the following configuration:\n"
" - Arm hanging straight down\n"
" - Gripper closed\n"
"Press ENTER when ready..."
)
homing_offsets = bus.set_half_turn_homings()
logger.info(f"{arm_name.capitalize()} arm zero position set.")
homing_offsets = self.bus.set_half_turn_homings()
logger.info("Arm zero position set.")
print(f"\nSetting motor ranges for {arm_name.upper()} arm\n")
print("\nSetting motor ranges\n")
if self.calibration is None:
self.calibration = {}
motor_resolution = bus.model_resolution_table[list(bus.motors.values())[0].model]
motor_resolution = self.bus.model_resolution_table[list(self.bus.motors.values())[0].model]
max_res = motor_resolution - 1
for motor_name, motor in bus.motors.items():
prefixed_name = f"{arm_name}_{motor_name}"
for motor_name, motor in self.bus.motors.items():
if motor_name == "gripper":
input(
f"\nGripper Calibration ({arm_name.upper()} arm)\n"
f"Step 1: CLOSE the gripper fully\n"
f"Press ENTER when gripper is closed..."
"\nGripper Calibration\n"
"Step 1: CLOSE the gripper fully\n"
"Press ENTER when gripper is closed..."
)
closed_pos = bus.read("Present_Position", motor_name, normalize=False)
closed_pos = self.bus.read("Present_Position", motor_name, normalize=False)
logger.info(f" Gripper closed position recorded: {closed_pos}")
input("\nStep 2: OPEN the gripper fully\nPress ENTER when gripper is fully open...")
open_pos = bus.read("Present_Position", motor_name, normalize=False)
open_pos = self.bus.read("Present_Position", motor_name, normalize=False)
logger.info(f" Gripper open position recorded: {open_pos}")
if closed_pos < open_pos:
@@ -228,16 +179,16 @@ class OpenArmMini(Teleoperator):
drive_mode = 1
logger.info(
f" {prefixed_name}: range set to [{range_min}, {range_max}] "
f" {motor_name}: range set to [{range_min}, {range_max}] "
f"(0=closed, 100=open, drive_mode={drive_mode})"
)
else:
range_min = 0
range_max = max_res
drive_mode = 0
logger.info(f" {prefixed_name}: range set to [0, {max_res}] (full motor range)")
logger.info(f" {motor_name}: range set to [0, {max_res}] (full motor range)")
self.calibration[prefixed_name] = MotorCalibration(
self.calibration[motor_name] = MotorCalibration(
id=motor.id,
drive_mode=drive_mode,
homing_offset=homing_offsets[motor_name],
@@ -245,108 +196,68 @@ class OpenArmMini(Teleoperator):
range_max=range_max,
)
cal_for_bus = {
k.replace(f"{arm_name}_", ""): v
for k, v in self.calibration.items()
if k.startswith(f"{arm_name}_")
}
bus.write_calibration(cal_for_bus)
self.bus.write_calibration(self.calibration)
self._save_calibration()
print(f"\nCalibration complete and saved to {self.calibration_fpath}")
def configure(self) -> None:
self.bus_right.disable_torque()
self.bus_right.configure_motors()
for motor in self.bus_right.motors:
self.bus_right.write("Operating_Mode", motor, OperatingMode.POSITION.value)
self.bus_left.disable_torque()
self.bus_left.configure_motors()
for motor in self.bus_left.motors:
self.bus_left.write("Operating_Mode", motor, OperatingMode.POSITION.value)
self.bus.disable_torque()
self.bus.configure_motors()
for motor in self.bus.motors:
self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
def setup_motors(self) -> None:
print("\nSetting up RIGHT arm motors...")
for motor in reversed(self.bus_right.motors):
input(f"Connect the controller board to the RIGHT '{motor}' motor only and press enter.")
self.bus_right.setup_motor(motor)
print(f"RIGHT '{motor}' motor id set to {self.bus_right.motors[motor].id}")
print("\nSetting up LEFT arm motors...")
for motor in reversed(self.bus_left.motors):
input(f"Connect the controller board to the LEFT '{motor}' motor only and press enter.")
self.bus_left.setup_motor(motor)
print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")
for motor in reversed(self.bus.motors):
input(f"Connect the controller board to the '{motor}' motor only and press enter.")
self.bus.setup_motor(motor)
print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
@check_if_not_connected
def get_action(self) -> RobotAction:
"""Get current action from both arms (read positions from all motors)."""
"""Get current action (read positions from all motors)."""
start = time.perf_counter()
right_positions = self.bus_right.sync_read("Present_Position")
left_positions = self.bus_left.sync_read("Present_Position")
positions = self.bus.sync_read("Present_Position")
# Right first, then left — matches the robot (BiOpenArmFollower) ordering
# and the dataset feature names recorded during data collection.
# Joint 6↔7 remap: leader joint_6 → follower joint_7 and vice versa.
# Per-side direction flip is applied based on the configured `side`.
action: dict[str, Any] = {}
for motor, val in right_positions.items():
for motor, val in positions.items():
target = JOINT_REMAP.get(motor, motor)
if motor == "gripper":
# Convert gripper from teleop 0-100 to openarms degrees: 0→0°, 100→-65°
action[f"right_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
action[f"{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
else:
action[f"right_{target}.pos"] = -val if motor in RIGHT_MOTORS_TO_FLIP else val
for motor, val in left_positions.items():
target = JOINT_REMAP.get(motor, motor)
if motor == "gripper":
action[f"left_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
else:
action[f"left_{target}.pos"] = -val if motor in LEFT_MOTORS_TO_FLIP else val
action[f"{target}.pos"] = -val if motor in self._motors_to_flip else val
dt_ms = (time.perf_counter() - start) * 1e3
logger.debug(f"{self} read action: {dt_ms:.1f}ms")
return action
def enable_torque(self) -> None:
"""Enable torque on both arms for position control."""
self.bus_right.enable_torque()
self.bus_left.enable_torque()
self.bus.enable_torque()
def disable_torque(self) -> None:
"""Disable torque on both arms for free movement."""
self.bus_right.disable_torque()
self.bus_left.disable_torque()
self.bus.disable_torque()
def write_goal_positions(self, positions: dict[str, float]) -> None:
"""Write goal positions to motors (inverse of get_action flip/gripper/remap logic)."""
right_goals: dict[str, float] = {}
left_goals: dict[str, float] = {}
goals: dict[str, float] = {}
for key, val in positions.items():
if not key.endswith(".pos"):
continue
motor_name = key.removesuffix(".pos")
if motor_name.startswith("right_"):
base = motor_name.removeprefix("right_")
# Reverse remap: follower joint_7 → leader joint_6 and vice versa
target = JOINT_REMAP_REVERSE.get(base, base)
if base == "gripper":
# Convert robot degrees to teleop 0-100: 0°→0, -65°→100
right_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
else:
# Un-flip using the ORIGINAL motor name (target = leader motor)
right_goals[target] = -val if target in RIGHT_MOTORS_TO_FLIP else val
elif motor_name.startswith("left_"):
base = motor_name.removeprefix("left_")
target = JOINT_REMAP_REVERSE.get(base, base)
if base == "gripper":
left_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
else:
left_goals[target] = -val if target in LEFT_MOTORS_TO_FLIP else val
base = key.removesuffix(".pos")
# JOINT_REMAP is symmetric (its own inverse).
target = JOINT_REMAP.get(base, base)
if base == "gripper":
# Convert robot degrees to teleop 0-100: 0°→0, -65°→100
goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
else:
# Un-flip using the ORIGINAL motor name (target = leader motor)
goals[target] = -val if target in self._motors_to_flip else val
if right_goals:
self.bus_right.sync_write("Goal_Position", right_goals)
if left_goals:
self.bus_left.sync_write("Goal_Position", left_goals)
if goals:
self.bus.sync_write("Goal_Position", goals)
@check_if_not_connected
def send_feedback(self, feedback: dict[str, float]) -> None:
@@ -354,6 +265,5 @@ class OpenArmMini(Teleoperator):
@check_if_not_connected
def disconnect(self) -> None:
self.bus_right.disconnect()
self.bus_left.disconnect()
self.bus.disconnect()
logger.info(f"{self} disconnected.")
+6 -2
View File
@@ -99,14 +99,18 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
from .openarm_mini import OpenArmMini
return OpenArmMini(config)
elif config.type == "bi_openarm_mini":
from .bi_openarm_mini import BiOpenArmMini
return BiOpenArmMini(config)
elif config.type == "rebot_102_leader":
from .rebot_102_leader import RebotArm102Leader
return RebotArm102Leader(config)
elif config.type == "bi_rebot_102_leader":
from .bi_rebot_102_leader import BiRebotArm102Leader
from .bi_rebot_102_leader import BiRebot102Leader
return BiRebotArm102Leader(config)
return BiRebot102Leader(config)
else:
try:
return cast("Teleoperator", make_device_from_device_class(config))
+63
View File
@@ -0,0 +1,63 @@
#!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
class BimanualMixin:
"""Lifecycle delegation for bimanual robots and teleoperators.
Concrete subclasses must populate ``self.left_arm`` and ``self.right_arm`` in
their own ``__init__``. They retain ownership of feature dicts and the
data-routing methods (``get_action`` / ``send_action`` / ``get_observation`` /
``send_feedback``), which vary per-embodiment.
Inherit before the ``Robot`` / ``Teleoperator`` base so the mixin's methods
take precedence in the MRO::
class BiFooFollower(BimanualMixin, Robot): ...
"""
left_arm: Any
right_arm: Any
@property
def is_connected(self) -> bool:
return self.left_arm.is_connected and self.right_arm.is_connected
@property
def is_calibrated(self) -> bool:
return self.left_arm.is_calibrated and self.right_arm.is_calibrated
@check_if_already_connected
def connect(self, calibrate: bool = True) -> None:
self.left_arm.connect(calibrate)
self.right_arm.connect(calibrate)
def calibrate(self) -> None:
self.left_arm.calibrate()
self.right_arm.calibrate()
def configure(self) -> None:
self.left_arm.configure()
self.right_arm.configure()
@check_if_not_connected
def disconnect(self) -> None:
self.left_arm.disconnect()
self.right_arm.disconnect()
+73
View File
@@ -28,6 +28,7 @@ import pytest
pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
pytest.importorskip("pandas", reason="pandas is required (install lerobot[dataset])")
import pandas as pd # noqa: E402
import pyarrow.parquet as pq # noqa: E402
from lerobot.annotations.steerable_pipeline.reader import iter_episodes # noqa: E402
@@ -344,6 +345,78 @@ def test_annotation_metadata_sync_allows_non_streaming_load(
assert len(dataset) == 24
def _build_packed_dataset(root: Path, episode_lengths: list[int], *, fps: int = 10) -> Path:
"""Pack several episodes into a single shard (vs build_annotation_dataset's one-per-file),
so the writer's rewrite must re-emit one row group per episode instead of collapsing them."""
from lerobot.datasets.io_utils import write_tasks
from lerobot.utils.io_utils import write_json
data_dir = root / "data" / "chunk-000"
data_dir.mkdir(parents=True, exist_ok=True)
episode_index, frame_index, timestamp, task_index, subtask_index = [], [], [], [], []
for ep, length in enumerate(episode_lengths):
episode_index += [ep] * length
frame_index += list(range(length))
timestamp += [round(i / fps, 6) for i in range(length)]
task_index += [0] * length
subtask_index += [0] * length # legacy column the writer must drop
pd.DataFrame(
{
"episode_index": episode_index,
"frame_index": frame_index,
"timestamp": timestamp,
"task_index": task_index,
"subtask_index": subtask_index,
}
).to_parquet(data_dir / "file-000.parquet", index=False)
tasks_df = pd.DataFrame({"task_index": [0]}, index=pd.Index(["do the thing"], name="task"))
write_tasks(tasks_df, root)
write_json(
{"codebase_version": "v3.1", "fps": fps, "features": {}, "total_episodes": len(episode_lengths)},
root / "meta" / "info.json",
)
return root
def test_writer_one_row_group_per_episode(tmp_path: Path) -> None:
"""Rewriting a packed shard must keep one row group per episode, not collapse
every episode into a single giant row group."""
episode_lengths = [4, 6, 5] # unequal lengths, all in one shard
root = _build_packed_dataset(tmp_path / "ds", episode_lengths)
shard = root / "data" / "chunk-000" / "file-000.parquet"
assert pq.ParquetFile(shard).metadata.num_row_groups == 1, "fixture should start collapsed"
staging_dir = tmp_path / "stage"
for ep in range(len(episode_lengths)):
_stage_episode(
staging_dir,
ep,
plan=[
{
"role": "assistant",
"content": f"subtask for ep {ep}",
"style": "subtask",
"timestamp": 0.0,
"tool_calls": None,
}
],
)
records = list(iter_episodes(root))
LanguageColumnsWriter().write_all(records, staging_dir, root)
# One row group per episode, with row counts matching the episode lengths.
md = pq.ParquetFile(shard).metadata
assert md.num_row_groups == len(episode_lengths)
assert [md.row_group(i).num_rows for i in range(md.num_row_groups)] == episode_lengths
# Language columns are still present after the per-episode rewrite.
table = pq.read_table(shard)
assert "language_persistent" in table.column_names
assert "language_events" in table.column_names
def test_speech_atom_shape_matches_plan_spec() -> None:
atom = speech_atom(2.5, "I'm cleaning up!")
assert atom["role"] == "assistant"
+55
View File
@@ -32,6 +32,26 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
from tests.fixtures.constants import DUMMY_REPO_ID
def assert_data_shards_one_row_group_per_episode(root):
"""Every aggregated DATA shard must have exactly one parquet row group per episode."""
import pyarrow.parquet as pq
shards = sorted((root / "data").rglob("*.parquet"))
assert shards, f"no data shards found under {root}/data"
n_episodes = 0
for shard in shards:
pf = pq.ParquetFile(shard)
episodes = pf.read(columns=["episode_index"]).column("episode_index").to_pylist()
assert pf.metadata.num_row_groups == len(set(episodes)), shard
for i in range(pf.metadata.num_row_groups):
rg_episodes = set(
pf.read_row_group(i, columns=["episode_index"]).column("episode_index").to_pylist()
)
assert len(rg_episodes) == 1, f"{shard} row group {i} spans episodes {rg_episodes}"
n_episodes += len(set(episodes))
return n_episodes
def assert_episode_and_frame_counts(aggr_ds, expected_episodes, expected_frames):
"""Test that total number of episodes and frames are correctly aggregated."""
assert aggr_ds.num_episodes == expected_episodes, (
@@ -566,6 +586,41 @@ def assert_image_frames_integrity(aggr_ds, ds_0, ds_1):
)
@pytest.mark.parametrize("use_videos", [True, False], ids=["video", "image"])
def test_aggregate_one_row_group_per_episode(tmp_path, lerobot_dataset_factory, use_videos):
"""Aggregated DATA shards keep one row group per episode (not one collapsed group).
Covers both the non-image (``df.to_parquet``) and image
(``to_parquet_with_hf_images``) write branches, including the merge-into-
existing-file branch via a low file-size threshold that forces packing.
"""
ds_0 = lerobot_dataset_factory(
root=tmp_path / "rg_0",
repo_id=f"{DUMMY_REPO_ID}_rg_0",
total_episodes=3,
total_frames=60,
use_videos=use_videos,
)
ds_1 = lerobot_dataset_factory(
root=tmp_path / "rg_1",
repo_id=f"{DUMMY_REPO_ID}_rg_1",
total_episodes=4,
total_frames=80,
use_videos=use_videos,
)
aggr_root = tmp_path / "rg_aggr"
aggregate_datasets(
repo_ids=[ds_0.repo_id, ds_1.repo_id],
roots=[ds_0.root, ds_1.root],
aggr_repo_id=f"{DUMMY_REPO_ID}_rg_aggr",
aggr_root=aggr_root,
)
n_episodes = assert_data_shards_one_row_group_per_episode(aggr_root)
assert n_episodes == ds_0.num_episodes + ds_1.num_episodes
def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
"""Test aggregation of image-based datasets preserves HuggingFace Image schema.
+16 -1
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@@ -51,7 +51,7 @@ from lerobot.robots import make_robot_from_config
from lerobot.transforms import ImageTransforms, ImageTransformsConfig
from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD
from lerobot.utils.feature_utils import hw_to_dataset_features
from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_MOTOR_FEATURES, DUMMY_REPO_ID
from tests.mocks.mock_robot import MockRobotConfig
from tests.utils import require_x86_64_kernel
@@ -133,6 +133,21 @@ def test_dataset_feature_with_forward_slash_raises_error():
)
def test_create_does_not_mutate_input_features(tmp_path, empty_lerobot_dataset_factory):
# ``create`` must deep-copy features so a dataset built from another's features stays independent.
dataset = empty_lerobot_dataset_factory(
root=tmp_path / "ds1", features=DUMMY_MOTOR_FEATURES, use_videos=False
)
dataset_copy = empty_lerobot_dataset_factory(
root=tmp_path / "ds2", features=dataset.meta.features, use_videos=False
)
original_shape = dataset.meta.info.features["state"]["shape"]
dataset_copy.meta.info.features["state"]["shape"] = (999,)
assert dataset.meta.info.features["state"]["shape"] == original_shape
def test_add_frame_missing_task(tmp_path, empty_lerobot_dataset_factory):
features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
+21 -3
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@@ -2370,14 +2370,32 @@ def test_aggregate_images_when_use_videos_false():
out = aggregate_pipeline_dataset_features(
pipeline=rp,
initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
use_videos=False, # expect "image" dtype
use_videos=False, # images kept, stored as "image" dtype
patterns=None,
)
key = f"{OBS_IMAGES}.back"
key_front = f"{OBS_IMAGES}.front"
assert key not in out
assert key_front not in out
assert key in out
assert key_front in out
assert out[key]["dtype"] == "image"
assert out[key_front]["dtype"] == "image"
assert out[key]["shape"] == initial["back"]
def test_aggregate_images_excluded():
rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
initial = {"back": (480, 640, 3)}
out = aggregate_pipeline_dataset_features(
pipeline=rp,
initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
exclude_images=True,
patterns=None,
)
assert f"{OBS_IMAGES}.back" not in out
assert f"{OBS_IMAGES}.front" not in out
def test_aggregate_images_when_use_videos_true():
+3 -3
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@@ -18,7 +18,7 @@ from unittest.mock import MagicMock, patch
import pytest
from lerobot.teleoperators.bi_rebot_102_leader import BiRebotArm102Leader, BiRebotArm102LeaderConfig
from lerobot.teleoperators.bi_rebot_102_leader import BiRebot102Leader, BiRebot102LeaderConfig
from lerobot.teleoperators.rebot_102_leader import (
RebotArm102Leader,
RebotArm102LeaderConfig,
@@ -91,11 +91,11 @@ def test_send_feedback_not_implemented(leader):
def test_bimanual_prefixes_features():
with patch(f"{_MODULE}.require_package", lambda *a, **kw: None):
cfg = BiRebotArm102LeaderConfig(
cfg = BiRebot102LeaderConfig(
left_arm_config=RebotArm102LeaderConfig(port="/dev/null0"),
right_arm_config=RebotArm102LeaderConfig(port="/dev/null1"),
)
teleop = BiRebotArm102Leader(cfg)
teleop = BiRebot102Leader(cfg)
assert any(k.startswith("left_") for k in teleop.action_features)
assert any(k.startswith("right_") for k in teleop.action_features)
assert "left_gripper.pos" in teleop.action_features
Generated
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