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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/hilserl.mdx
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@@ -50,12 +50,12 @@ pip install -e ".[hilserl]"
### Understanding Configuration
The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/common/envs/configs.py`. Which is defined as:
The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
```python
class HILSerlRobotEnvConfig(EnvConfig):
robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/common/robots`)
teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/common/teleoperators`)
robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/robots`)
teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
wrapper: EnvTransformConfig | None = None # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
fps: int = 10 # Control frequency
name: str = "real_robot" # Environment name
@@ -172,7 +172,7 @@ class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
)
```
The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/common/teleoperators`.
The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
**Setting up the Gamepad**
@@ -226,7 +226,7 @@ During the online training, press `space` to take over the policy and `space` ag
Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json):
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config_so100.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json
```
During recording:
@@ -256,7 +256,7 @@ Note: If you already know the crop parameters, you can skip this step and just s
Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
```bash
python lerobot/scripts/rl/crop_dataset_roi.py --repo-id username/pick_lift_cube
python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube
```
1. For each camera view, the script will display the first frame
@@ -313,7 +313,7 @@ Before training, you need to collect a dataset with labeled examples. The `recor
To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/reward_classifier_train_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json
```
**Key Parameters for Data Collection**
@@ -387,7 +387,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
To train the classifier, use the `train.py` script with your configuration:
```bash
python lerobot/scripts/train.py --config_path path/to/reward_classifier_train_config.json
python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
```
**Deploying and Testing the Model**
@@ -410,7 +410,7 @@ or set the argument in the json config file.
Run `gym_manipulator.py` to test the model.
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json
```
The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
@@ -422,17 +422,17 @@ The reward classifier will automatically provide rewards based on the visual inp
2. **Collect a dataset**:
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
```
3. **Train the classifier**:
```bash
python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
```
4. **Test the classifier**:
```bash
python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
```
### Training with Actor-Learner
@@ -446,7 +446,7 @@ Create a training configuration file (example available [here](https://huggingfa
1. Configure the policy settings (`type="sac"`, `device`, etc.)
2. Set `dataset` to your cropped dataset
3. Configure environment settings with crop parameters
4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/common/policies/sac/configuration_sac.py#L79).
4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/policies/sac/configuration_sac.py#L79).
5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
**Starting the Learner**
@@ -454,7 +454,7 @@ Create a training configuration file (example available [here](https://huggingfa
First, start the learner server process:
```bash
python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json
python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
```
The learner:
@@ -468,7 +468,7 @@ The learner:
In a separate terminal, start the actor process with the same configuration:
```bash
python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json
python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json
```
The actor: