@@ -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 \
|
||||
|
||||
Reference in New Issue
Block a user