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docs: fix wording in guides (#3939)

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Signed-off-by: aineoae86-sys <ai.neo.ae86@gmail.com>
Co-authored-by: aineoae86-sys <ai.neo.ae86@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
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@@ -22,7 +22,7 @@ With processors, you choose the learning features you want to use for your polic
## Three pipelines
We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
Each of these pipelines handles different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
@@ -74,15 +74,15 @@ In the phone to SO-100 follower examples we use the following adapters:
- `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition.
- `transition_to_robot_action`: transforms the pipeline transition to a robot action dict.
- `observation_to_transition`: transforms the robot observation dict to a pipeline transition.
- `transition_to_observation`: transforms the pipeline transition to a observation dict.
- `transition_to_observation`: transforms the pipeline transition to an observation dict.
Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
Check out [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
## Dataset feature contracts
Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`.
Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
Below is an example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
```python
def transform_features(