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Merge branch 'main' into user/michel-aractingi/2024-11-27-port-hil-serl

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Michel Aractingi
2024-12-10 16:02:49 +01:00
parent abcf17a070 44f9b21e74
commit 839dac9cd4
27 changed files with 752 additions and 2031 deletions
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9
examples/10_use_so100.md
View File

@@ -192,7 +192,6 @@ Record 2 episodes and upload your dataset to the hub:
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/so100.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/so100_test \
--tags so100 tutorial \
--warmup-time-s 5 \
@@ -212,7 +211,6 @@ echo ${HF_USER}/so100_test
If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
```bash
python lerobot/scripts/visualize_dataset_html.py \
--root data \
--repo-id ${HF_USER}/so100_test
```
@@ -220,10 +218,9 @@ python lerobot/scripts/visualize_dataset_html.py \
Now try to replay the first episode on your robot:
```bash
DATA_DIR=data python lerobot/scripts/control_robot.py replay \
python lerobot/scripts/control_robot.py replay \
--robot-path lerobot/configs/robot/so100.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/so100_test \
--episode 0
```
@@ -232,7 +229,7 @@ DATA_DIR=data python lerobot/scripts/control_robot.py replay \
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
DATA_DIR=data python lerobot/scripts/train.py \
python lerobot/scripts/train.py \
dataset_repo_id=${HF_USER}/so100_test \
policy=act_so100_real \
env=so100_real \
@@ -248,7 +245,6 @@ Let's explain it:
3. We provided an environment as argument with `env=so100_real`. This loads configurations from [`lerobot/configs/env/so100_real.yaml`](../lerobot/configs/env/so100_real.yaml).
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you can also use `device=mps` if you are using a Mac with Apple silicon, or `device=cpu` otherwise.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.
@@ -259,7 +255,6 @@ You can use the `record` function from [`lerobot/scripts/control_robot.py`](../l
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/so100.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/eval_act_so100_test \
--tags so100 tutorial eval \
--warmup-time-s 5 \

9
examples/11_use_moss.md
View File

@@ -192,7 +192,6 @@ Record 2 episodes and upload your dataset to the hub:
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/moss.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/moss_test \
--tags moss tutorial \
--warmup-time-s 5 \
@@ -212,7 +211,6 @@ echo ${HF_USER}/moss_test
If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
```bash
python lerobot/scripts/visualize_dataset_html.py \
--root data \
--repo-id ${HF_USER}/moss_test
```
@@ -220,10 +218,9 @@ python lerobot/scripts/visualize_dataset_html.py \
Now try to replay the first episode on your robot:
```bash
DATA_DIR=data python lerobot/scripts/control_robot.py replay \
python lerobot/scripts/control_robot.py replay \
--robot-path lerobot/configs/robot/moss.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/moss_test \
--episode 0
```
@@ -232,7 +229,7 @@ DATA_DIR=data python lerobot/scripts/control_robot.py replay \
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
DATA_DIR=data python lerobot/scripts/train.py \
python lerobot/scripts/train.py \
dataset_repo_id=${HF_USER}/moss_test \
policy=act_moss_real \
env=moss_real \
@@ -248,7 +245,6 @@ Let's explain it:
3. We provided an environment as argument with `env=moss_real`. This loads configurations from [`lerobot/configs/env/moss_real.yaml`](../lerobot/configs/env/moss_real.yaml).
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you can also use `device=mps` if you are using a Mac with Apple silicon, or `device=cpu` otherwise.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.
@@ -259,7 +255,6 @@ You can use the `record` function from [`lerobot/scripts/control_robot.py`](../l
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/moss.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/eval_act_moss_test \
--tags moss tutorial eval \
--warmup-time-s 5 \

12
examples/7_get_started_with_real_robot.md
View File

@@ -29,7 +29,7 @@ For a visual walkthrough of the assembly process, you can refer to [this video t
## 2. Configure motors, calibrate arms, teleoperate your Koch v1.1
First, install the additional dependencies required for robots built with dynamixel motors like Koch v1.1 by running one of the following commands.
First, install the additional dependencies required for robots built with dynamixel motors like Koch v1.1 by running one of the following commands (make sure gcc is installed).
Using `pip`:
```bash
@@ -778,7 +778,6 @@ Now run this to record 2 episodes:
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/koch.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/koch_test \
--tags tutorial \
--warmup-time-s 5 \
@@ -787,7 +786,7 @@ python lerobot/scripts/control_robot.py record \
--num-episodes 2
```
This will write your dataset locally to `{root}/{repo-id}` (e.g. `data/cadene/koch_test`) and push it on the hub at `https://huggingface.co/datasets/{HF_USER}/{repo-id}`. Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
This will write your dataset locally to `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/koch_test`) and push it on the hub at `https://huggingface.co/datasets/{HF_USER}/{repo-id}`. Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
You can look for other LeRobot datasets on the hub by searching for `LeRobot` tags: https://huggingface.co/datasets?other=LeRobot
@@ -840,7 +839,6 @@ In the coming months, we plan to release a foundational model for robotics. We a
You can visualize your dataset by running:
```bash
python lerobot/scripts/visualize_dataset_html.py \
--root data \
--repo-id ${HF_USER}/koch_test
```
@@ -858,7 +856,6 @@ To replay the first episode of the dataset you just recorded, run the following
python lerobot/scripts/control_robot.py replay \
--robot-path lerobot/configs/robot/koch.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/koch_test \
--episode 0
```
@@ -871,7 +868,7 @@ Your robot should replicate movements similar to those you recorded. For example
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
DATA_DIR=data python lerobot/scripts/train.py \
python lerobot/scripts/train.py \
dataset_repo_id=${HF_USER}/koch_test \
policy=act_koch_real \
env=koch_real \
@@ -918,7 +915,6 @@ env:
It should match your dataset (e.g. `fps: 30`) and your robot (e.g. `state_dim: 6` and `action_dim: 6`). We are still working on simplifying this in future versions of `lerobot`.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)
@@ -991,7 +987,6 @@ To this end, you can use the `record` function from [`lerobot/scripts/control_ro
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/koch.yaml \
--fps 30 \
--root data \
--repo-id ${HF_USER}/eval_koch_test \
--tags tutorial eval \
--warmup-time-s 5 \
@@ -1010,7 +1005,6 @@ As you can see, it's almost the same command as previously used to record your t
You can then visualize your evaluation dataset by running the same command as before but with the new inference dataset as argument:
```bash
python lerobot/scripts/visualize_dataset.py \
--root data \
--repo-id ${HF_USER}/eval_koch_test
```

2
examples/8_use_stretch.md
View File

@@ -128,7 +128,6 @@ Record one episode:
python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/stretch.yaml \
--fps 20 \
--root data \
--repo-id ${HF_USER}/stretch_test \
--tags stretch tutorial \
--warmup-time-s 3 \
@@ -146,7 +145,6 @@ Now try to replay this episode (make sure the robot's initial position is the sa
python lerobot/scripts/control_robot.py replay \
--robot-path lerobot/configs/robot/stretch.yaml \
--fps 20 \
--root data \
--repo-id ${HF_USER}/stretch_test \
--episode 0
```

7
examples/9_use_aloha.md
View File

@@ -84,7 +84,6 @@ python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/aloha.yaml \
--robot-overrides max_relative_target=null \
--fps 30 \
--root data \
--repo-id ${HF_USER}/aloha_test \
--tags aloha tutorial \
--warmup-time-s 5 \
@@ -104,7 +103,6 @@ echo ${HF_USER}/aloha_test
If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
```bash
python lerobot/scripts/visualize_dataset_html.py \
--root data \
--repo-id ${HF_USER}/aloha_test
```
@@ -119,7 +117,6 @@ python lerobot/scripts/control_robot.py replay \
--robot-path lerobot/configs/robot/aloha.yaml \
--robot-overrides max_relative_target=null \
--fps 30 \
--root data \
--repo-id ${HF_USER}/aloha_test \
--episode 0
```
@@ -128,7 +125,7 @@ python lerobot/scripts/control_robot.py replay \
To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
DATA_DIR=data python lerobot/scripts/train.py \
python lerobot/scripts/train.py \
dataset_repo_id=${HF_USER}/aloha_test \
policy=act_aloha_real \
env=aloha_real \
@@ -144,7 +141,6 @@ Let's explain it:
3. We provided an environment as argument with `env=aloha_real`. This loads configurations from [`lerobot/configs/env/aloha_real.yaml`](../lerobot/configs/env/aloha_real.yaml). Note: this yaml defines 18 dimensions for the `state_dim` and `action_dim`, corresponding to 18 motors, not 14 motors as used in previous Aloha work. This is because, we include the `shoulder_shadow` and `elbow_shadow` motors for simplicity.
4. We provided `device=cuda` since we are training on a Nvidia GPU.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
Training should take several hours. You will find checkpoints in `outputs/train/act_aloha_test/checkpoints`.
@@ -156,7 +152,6 @@ python lerobot/scripts/control_robot.py record \
--robot-path lerobot/configs/robot/aloha.yaml \
--robot-overrides max_relative_target=null \
--fps 30 \
--root data \
--repo-id ${HF_USER}/eval_act_aloha_test \
--tags aloha tutorial eval \
--warmup-time-s 5 \

2
examples/port_datasets/pusht_zarr.py
View File

@@ -63,7 +63,7 @@ def build_features(mode: str) -> dict:
return features
def load_raw_dataset(zarr_path: Path, load_images: bool = True):
def load_raw_dataset(zarr_path: Path):
try:
from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
ReplayBuffer as DiffusionPolicyReplayBuffer,