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Package folder structure (#1417)

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* Move files

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* Update relative paths

* Update doc symlinks

* Update instructions paths

* Fix imports

* Update grpc files

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* Downgrade grpc-tools

* Update manifest

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* Update config paths

* Update CI paths

* Update bandit exclusions

* Remove walkthrough section
This commit is contained in:
Simon Alibert
2025-07-01 16:34:46 +02:00
committed by GitHub
parent 483be9aac2
commit d4ee470b00
268 changed files with 862 additions and 890 deletions
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docs/source/il_sim.mdx
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@@ -35,14 +35,14 @@ Then we can run this command to start:
<hfoption id="Linux">
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
```
</hfoption>
<hfoption id="MacOS">
```bash
mjpython lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json
mjpython -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
```
</hfoption>
@@ -81,9 +81,9 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](
## 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:
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python lerobot/scripts/train.py \
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/il_gym \
--policy.type=act \
--output_dir=outputs/train/il_sim_test \
@@ -94,7 +94,7 @@ python lerobot/scripts/train.py \
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.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`.
@@ -130,14 +130,14 @@ Then you can run this command to visualize your trained policy
<hfoption id="Linux">
```bash
python lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json
python -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
```
</hfoption>
<hfoption id="MacOS">
```bash
mjpython lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json
mjpython -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
```
</hfoption>