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LeRobotDataset v2.1 (#711)

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Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: Remi Cadene <re.cadene@gmail.com>
This commit is contained in:
Simon Alibert
2025-02-25 15:27:29 +01:00
committed by GitHub
parent aca464ca72
commit 3354d919fc
43 changed files with 2023 additions and 1322 deletions
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6
examples/10_use_so100.md
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@@ -335,7 +335,7 @@ python lerobot/scripts/control_robot.py \
--control.push_to_hub=true
```
Note: You can resume recording by adding `--control.resume=true`. Also if you didn't push your dataset yet, add `--control.local_files_only=true`.
Note: You can resume recording by adding `--control.resume=true`.
## H. Visualize a dataset
@@ -363,8 +363,6 @@ python lerobot/scripts/control_robot.py \
--control.episode=0
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
## J. Train a policy
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
@@ -378,8 +376,6 @@ python lerobot/scripts/train.py \
--wandb.enable=true
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.

6
examples/11_use_lekiwi.md
View File

@@ -391,7 +391,7 @@ python lerobot/scripts/control_robot.py \
--control.push_to_hub=true
```
Note: You can resume recording by adding `--control.resume=true`. Also if you didn't push your dataset yet, add `--control.local_files_only=true`.
Note: You can resume recording by adding `--control.resume=true`.
# H. Visualize a dataset
@@ -418,8 +418,6 @@ python lerobot/scripts/control_robot.py \
--control.episode=0
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
## J. Train a policy
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
@@ -433,8 +431,6 @@ python lerobot/scripts/train.py \
--wandb.enable=true
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.

6
examples/11_use_moss.md
View File

@@ -256,7 +256,7 @@ python lerobot/scripts/control_robot.py \
--control.push_to_hub=true
```
Note: You can resume recording by adding `--control.resume=true`. Also if you didn't push your dataset yet, add `--control.local_files_only=true`.
Note: You can resume recording by adding `--control.resume=true`.
## Visualize a dataset
@@ -284,8 +284,6 @@ python lerobot/scripts/control_robot.py \
--control.episode=0
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
## Train a policy
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
@@ -299,8 +297,6 @@ python lerobot/scripts/train.py \
--wandb.enable=true
```
Note: If you didn't push your dataset yet, add `--control.local_files_only=true`.
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.

6
examples/7_get_started_with_real_robot.md
View File

@@ -768,7 +768,7 @@ You can use the `record` function from [`lerobot/scripts/control_robot.py`](../l
1. Frames from cameras are saved on disk in threads, and encoded into videos at the end of each episode recording.
2. Video streams from cameras are displayed in window so that you can verify them.
3. Data is stored with [`LeRobotDataset`](../lerobot/common/datasets/lerobot_dataset.py) format which is pushed to your Hugging Face page (unless `--control.push_to_hub=false` is provided).
4. Checkpoints are done during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You might need to add `--control.local_files_only=true` if your dataset was not uploaded to hugging face hub. Also you will need to manually delete the dataset directory to start recording from scratch.
4. Checkpoints are done during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You will need to manually delete the dataset directory if you want to start recording from scratch.
5. Set the flow of data recording using command line arguments:
- `--control.warmup_time_s=10` defines the number of seconds before starting data collection. It allows the robot devices to warmup and synchronize (10 seconds by default).
- `--control.episode_time_s=60` defines the number of seconds for data recording for each episode (60 seconds by default).
@@ -883,8 +883,6 @@ python lerobot/scripts/control_robot.py \
--control.episode=0
```
Note: You might need to add `--control.local_files_only=true` if your dataset was not uploaded to hugging face hub.
Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
## 4. Train a policy on your data
@@ -902,8 +900,6 @@ python lerobot/scripts/train.py \
--wandb.enable=true
```
Note: You might need to add `--dataset.local_files_only=true` if your dataset was not uploaded to hugging face hub.
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/koch_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.

41
examples/port_datasets/pusht_zarr.py
View File

@@ -2,9 +2,10 @@ import shutil
from pathlib import Path
import numpy as np
import torch
from huggingface_hub import HfApi
from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset
from lerobot.common.constants import HF_LEROBOT_HOME
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface."
@@ -89,9 +90,9 @@ def calculate_coverage(zarr_data):
num_frames = len(block_pos)
coverage = np.zeros((num_frames,))
coverage = np.zeros((num_frames,), dtype=np.float32)
# 8 keypoints with 2 coords each
keypoints = np.zeros((num_frames, 16))
keypoints = np.zeros((num_frames, 16), dtype=np.float32)
# Set x, y, theta (in radians)
goal_pos_angle = np.array([256, 256, np.pi / 4])
@@ -117,7 +118,7 @@ def calculate_coverage(zarr_data):
intersection_area = goal_geom.intersection(block_geom).area
goal_area = goal_geom.area
coverage[i] = intersection_area / goal_area
keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten())
keypoints[i] = PushTEnv.get_keypoints(block_shapes).flatten()
return coverage, keypoints
@@ -134,8 +135,8 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
if mode not in ["video", "image", "keypoints"]:
raise ValueError(mode)
if (LEROBOT_HOME / repo_id).exists():
shutil.rmtree(LEROBOT_HOME / repo_id)
if (HF_LEROBOT_HOME / repo_id).exists():
shutil.rmtree(HF_LEROBOT_HOME / repo_id)
if not raw_dir.exists():
download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
@@ -148,6 +149,10 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
action = zarr_data["action"][:]
image = zarr_data["img"] # (b, h, w, c)
if image.dtype == np.float32 and image.max() == np.float32(255):
# HACK: images are loaded as float32 but they actually encode uint8 data
image = image.astype(np.uint8)
episode_data_index = {
"from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])),
"to": zarr_data.meta["episode_ends"],
@@ -175,28 +180,30 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
for frame_idx in range(num_frames):
i = from_idx + frame_idx
idx = i + (frame_idx < num_frames - 1)
frame = {
"action": torch.from_numpy(action[i]),
"action": action[i],
# Shift reward and success by +1 until the last item of the episode
"next.reward": reward[i + (frame_idx < num_frames - 1)],
"next.success": success[i + (frame_idx < num_frames - 1)],
"next.reward": reward[idx : idx + 1],
"next.success": success[idx : idx + 1],
"task": PUSHT_TASK,
}
frame["observation.state"] = torch.from_numpy(agent_pos[i])
frame["observation.state"] = agent_pos[i]
if mode == "keypoints":
frame["observation.environment_state"] = torch.from_numpy(keypoints[i])
frame["observation.environment_state"] = keypoints[i]
else:
frame["observation.image"] = torch.from_numpy(image[i])
frame["observation.image"] = image[i]
dataset.add_frame(frame)
dataset.save_episode(task=PUSHT_TASK)
dataset.consolidate()
dataset.save_episode()
if push_to_hub:
dataset.push_to_hub()
hub_api = HfApi()
hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
if __name__ == "__main__":
@@ -218,5 +225,5 @@ if __name__ == "__main__":
main(raw_dir, repo_id=repo_id, mode=mode)
# Uncomment if you want to load the local dataset and explore it
# dataset = LeRobotDataset(repo_id=repo_id, local_files_only=True)
# dataset = LeRobotDataset(repo_id=repo_id)
# breakpoint()