Updates Mimic-Cosmos pipeline doc as per QA suggestions. (#477)

# Description

Fixes in Cosmos-Mimic (Augmented Imitation Learning) documentation:
1. Corrected the command specified for the Mimic data generation step to
use CPU instead of GPU.
2. Added link to specific Cosmos example to follow for Cosmos
generation/augmentation step.

## Type of change

- Bug fix (non-breaking change which fixes an issue)
- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

docs/source/overview/augmented_imitation.rst

@@ -21,7 +21,7 @@ In the following example, we will show you how to use Isaac Lab Mimic to generat
 .. code:: bash
 
     ./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py \
-    --device cuda --enable_cameras --headless --num_envs 10 --generation_num_trials 1000 \
+    --device cpu --enable_cameras --headless --num_envs 10 --generation_num_trials 1000 \
     --input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/mimic_dataset_1k.hdf5 \
     --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos-Mimic-v0 \
     --rendering_mode performance
@@ -99,7 +99,7 @@ We provide an example augmentation output from `Cosmos Transfer1 <https://github
    :align: center
    :alt: Cosmos Transfer1 augmentation output
 
-We recommend using the `Cosmos Transfer1 <https://github.com/nvidia-cosmos/cosmos-transfer1>`_ model for visual augmentation as we found it to produce the best results in the form of a highly diverse dataset with a wide range of visual variations. We further recommend the following settings to be used with the Transfer1 model for this task:
+We recommend using the `Cosmos Transfer1 <https://github.com/nvidia-cosmos/cosmos-transfer1>`_ model for visual augmentation as we found it to produce the best results in the form of a highly diverse dataset with a wide range of visual variations. You can refer to `this example <https://github.com/nvidia-cosmos/cosmos-transfer1/blob/main/examples/inference_cosmos_transfer1_7b.md#example-2-multimodal-control>`_ for reference on how to use Transfer1 for this usecase. We further recommend the following settings to be used with the Transfer1 model for this task:
 
 .. rubric:: Hyperparameters