Updates the mimic teleop doc to link to the locomotion policy training (#4053)

# Description
Add a short description about the locomotion policy used in the
loco-manipulation teleop demo. Added a link to the AGILE training
framework since it is released.

<!-- As a practice, it is recommended to open an issue to have
discussions on the proposed pull request.
This makes it easier for the community to keep track of what is being
developed or added, and if a given feature
is demanded by more than one party. -->

## Type of change
- Documentation update

## Screenshots

Please attach before and after screenshots of the change if applicable.

<!--
Example:

| Before | After |
| ------ | ----- |
| _gif/png before_ | _gif/png after_ |

To upload images to a PR -- simply drag and drop an image while in edit
mode and it should upload the image directly. You can then paste that
source into the above before/after sections.
-->

## Checklist

- [x] I have read and understood the [contribution
guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html)
- [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

<!--
As you go through the checklist above, you can mark something as done by
putting an x character in it

For example,
- [x] I have done this task
- [ ] I have not done this task
-->

Author: huihuaNvidia2023

docs/source/overview/imitation-learning/teleop_imitation.rst

@@ -546,6 +546,16 @@ The robot picks up an object at the initial location (point A) and places it at
    :alt: G1 humanoid robot with locomanipulation performing a pick and place task
    :figclass: align-center
 
+.. note::
+   **Locomotion policy training**
+
+   The locomotion policy used in this integration example was trained using the `AGILE <https://github.com/nvidia-isaac/WBC-AGILE>`__ framework.
+   AGILE is an officially supported humanoid control training pipeline that leverages the manager based environment in Isaac Lab. It will also be
+   seamlessly integrated with other evaluation and deployment tools across Isaac products. This allows teams to rely on a single, maintained stack
+   covering all necessary infrastructure and tooling for policy training, with easy export to real-world deployment. The AGILE repository contains
+   updated pre-trained policies with separate upper and lower body policies for flexibtility. They have been verified in the real world and can be
+   directly deployed. Users can also train their own locomotion or whole-body control policies using the AGILE framework.
+
 Generate the manipulation dataset
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^