mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-20 19:19:56 +00:00
Pepijn
184 lines
6.4 KiB
Python
184 lines
6.4 KiB
Python
#!/usr/bin/env python
|
||
|
||
# Copyright 2025 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.
|
||
|
||
"""
|
||
Add ``observation.state`` to an existing LeRobot dataset.
|
||
|
||
pi0 with ``use_relative_actions=True`` requires ``observation.state`` to
|
||
compute relative actions (action − state) on the fly. This script adds
|
||
that feature when it doesn't already exist.
|
||
|
||
Two modes for deriving ``observation.state``:
|
||
|
||
1. **From an existing feature** (``STATE_SOURCE_FEATURE``):
|
||
Copies an existing column (e.g. ``observation.joints`` or
|
||
``observation.pose``) to ``observation.state``.
|
||
|
||
2. **From action with offset** (``STATE_SOURCE_FEATURE = None``):
|
||
Derives state from the action column with a per-episode offset:
|
||
state[t] = action[t - STATE_ACTION_OFFSET]
|
||
|
||
After running this script, recompute relative action stats via the CLI:
|
||
|
||
lerobot-edit-dataset \\
|
||
--repo_id <your_dataset> \\
|
||
--operation.type recompute_stats \\
|
||
--operation.relative_action true \\
|
||
--operation.chunk_size 50 \\
|
||
--operation.relative_exclude_joints "['gripper']" \\
|
||
--push_to_hub true
|
||
|
||
Usage:
|
||
python convert_umi_dataset.py
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import logging
|
||
from collections.abc import Callable
|
||
|
||
import numpy as np
|
||
|
||
from lerobot.datasets.dataset_tools import add_features
|
||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||
|
||
logging.basicConfig(level=logging.INFO)
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
HF_DATASET_ID = ""
|
||
|
||
# Source for observation.state. Options:
|
||
# - A feature name (e.g. "observation.joints", "observation.pose") to copy
|
||
# an existing column. Must have the same shape as "action".
|
||
# - None to derive state from action with STATE_ACTION_OFFSET.
|
||
STATE_SOURCE_FEATURE: str | None = "observation.joints"
|
||
|
||
# Only used when STATE_SOURCE_FEATURE is None.
|
||
# 0 → state[t] = action[t] (same instant)
|
||
# 1 → state[t] = action[t-1] (state lags by 1 step)
|
||
STATE_ACTION_OFFSET = 1
|
||
|
||
# Push the augmented dataset to the Hugging Face Hub.
|
||
PUSH_TO_HUB = True
|
||
|
||
|
||
def _build_state_from_feature(dataset: LeRobotDataset, source_feature: str) -> Callable:
|
||
"""Return a callable that copies values from an existing feature."""
|
||
hf = dataset.hf_dataset
|
||
source_values = hf[source_feature]
|
||
|
||
episode_indices = np.array(hf["episode_index"])
|
||
frame_indices = np.array(hf["frame_index"])
|
||
key_to_global = {(int(episode_indices[i]), int(frame_indices[i])): i for i in range(len(episode_indices))}
|
||
|
||
def _get_state(row_dict: dict, ep_idx: int, frame_idx: int):
|
||
return source_values[key_to_global[(ep_idx, frame_idx)]]
|
||
|
||
return _get_state
|
||
|
||
|
||
def _build_state_from_action_offset(dataset: LeRobotDataset, offset: int) -> Callable:
|
||
"""Return a callable that derives state from action with a per-episode offset.
|
||
|
||
state[t] = action[max(0, t - offset)] (clamped to episode start)
|
||
"""
|
||
hf = dataset.hf_dataset
|
||
episode_indices = np.array(hf["episode_index"])
|
||
frame_indices = np.array(hf["frame_index"])
|
||
|
||
ep_sorted: dict[int, list[tuple[int, int]]] = {}
|
||
for ep_idx in np.unique(episode_indices):
|
||
ep_mask = episode_indices == ep_idx
|
||
ep_globals = np.where(ep_mask)[0]
|
||
ep_frames = frame_indices[ep_globals]
|
||
order = np.argsort(ep_frames)
|
||
ep_sorted[int(ep_idx)] = [(int(ep_frames[o]), int(ep_globals[o])) for o in order]
|
||
|
||
ep_frame_to_local: dict[int, dict[int, int]] = {}
|
||
for ep_idx, sorted_list in ep_sorted.items():
|
||
ep_frame_to_local[ep_idx] = {frame: local for local, (frame, _) in enumerate(sorted_list)}
|
||
|
||
actions = hf["action"]
|
||
|
||
def _get_state(row_dict: dict, ep_idx: int, frame_idx: int):
|
||
local_t = ep_frame_to_local[ep_idx][frame_idx]
|
||
source_local = max(0, local_t - offset)
|
||
_, source_global = ep_sorted[ep_idx][source_local]
|
||
return actions[source_global]
|
||
|
||
return _get_state
|
||
|
||
|
||
def main():
|
||
logger.info(f"Loading dataset {HF_DATASET_ID}")
|
||
dataset = LeRobotDataset(HF_DATASET_ID)
|
||
|
||
if "observation.state" in dataset.features:
|
||
logger.info("observation.state already exists — nothing to do")
|
||
return
|
||
|
||
action_meta = dataset.features["action"]
|
||
logger.info(f"Action shape: {action_meta['shape']}, names: {action_meta.get('names')}")
|
||
|
||
if STATE_SOURCE_FEATURE is not None:
|
||
if STATE_SOURCE_FEATURE not in dataset.features:
|
||
raise ValueError(
|
||
f"Source feature '{STATE_SOURCE_FEATURE}' not found. "
|
||
f"Available: {list(dataset.features.keys())}"
|
||
)
|
||
source_meta = dataset.features[STATE_SOURCE_FEATURE]
|
||
logger.info(f"Copying {STATE_SOURCE_FEATURE} → observation.state")
|
||
state_fn = _build_state_from_feature(dataset, STATE_SOURCE_FEATURE)
|
||
state_feature_info = {
|
||
"dtype": "float32",
|
||
"shape": list(source_meta["shape"]),
|
||
"names": source_meta.get("names"),
|
||
}
|
||
else:
|
||
logger.info(f"Deriving observation.state from action with offset={STATE_ACTION_OFFSET}")
|
||
state_fn = _build_state_from_action_offset(dataset, offset=STATE_ACTION_OFFSET)
|
||
state_feature_info = {
|
||
"dtype": "float32",
|
||
"shape": list(action_meta["shape"]),
|
||
"names": action_meta.get("names"),
|
||
}
|
||
|
||
augmented = add_features(
|
||
dataset,
|
||
features={"observation.state": (state_fn, state_feature_info)},
|
||
)
|
||
logger.info("observation.state added")
|
||
|
||
if PUSH_TO_HUB:
|
||
logger.info(f"Pushing to Hub: {augmented.repo_id}")
|
||
augmented.push_to_hub()
|
||
|
||
logger.info(
|
||
f"Done. Now recompute relative action stats:\n"
|
||
" lerobot-edit-dataset \\\n"
|
||
f" --repo_id {augmented.repo_id} \\\n"
|
||
" --operation.type recompute_stats \\\n"
|
||
" --operation.relative_action true \\\n"
|
||
" --operation.chunk_size 50 \\\n"
|
||
" --operation.relative_exclude_joints \"['gripper']\" \\\n"
|
||
" --push_to_hub true"
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|