Adds data gen and policy learning times in SkillGen documentation (#3773)

## Description

This PR updates the SkillGen docs to include expected data generation
and policy training times for clarity.

Dependencies: None

## Type of change

- Documentation update

## 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

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

@@ -232,6 +232,8 @@ Key parameters for SkillGen data generation:
 * ``--device``: Computation device (cpu/cuda). Use cpu for stable physics
 * ``--headless``: Disable visualization for faster generation
 
+.. _task-basic-cube-stacking:
+
 Task 1: Basic Cube Stacking
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -286,9 +288,11 @@ Once satisfied with small-scale results, generate a full training dataset:
 
    * Use ``--headless`` to disable visualization for faster generation. Rerun visualization can be enabled by setting ``visualize_plan = True`` in the cuRobo planner configuration with ``--headless`` enabled as well for debugging.
    * Adjust ``--num_envs`` based on your GPU memory (start with 1, increase gradually). The performance gain is not very significant when num_envs is greater than 1. A value of 5 seems to be a sweet spot for most GPUs to balance performance and memory usage between cuRobo instances and simulation environments.
-   * Generation time: ~90 to 120 minutes for one environment for 1000 demonstrations on modern GPUs. Time depends on the GPU, the number of environments, and the success rate of the demonstrations (which depends on quality of the annotated dataset).
+   * Generation time: ~90 to 120 minutes for one environment with ``--headless`` enabled for 1000 demonstrations on a RTX 6000 Ada GPU. Time depends on the GPU, the number of environments, and the success rate of the demonstrations (which depends on quality of the annotated dataset).
    * cuRobo planner interface and configurations are described in :ref:`cuRobo-interface-features`.
 
+.. _task-bin-cube-stacking:
+
 Task 2: Adaptive Cube Stacking in a Bin
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 SkillGen can also be used to generate datasets for adaptive tasks. In this example, we generate a dataset for adaptive cube stacking in a narrow bin. The bin is placed at a fixed position and orientation in the workspace and a blue cube is placed at the center of the bin. The robot must generate successful demonstrations for stacking the red and green cubes on the blue cube without colliding with the bin.
@@ -338,6 +342,10 @@ Generate the complete adaptive stacking dataset:
 
    Adaptive tasks typically have lower success rates and higher data generation time due to increased complexity. The time taken to generate the dataset is also longer due to lower success rates than vanilla cube stacking and difficult planning problems.
 
+.. note::
+
+   If the pre-annotated dataset is used and the data generation command is run with ``--headless`` enabled, the generation time is typically around ~220 minutes for 1000 demonstrations for a single environment on a RTX 6000 Ada GPU.
+
 
 Learning Policies from SkillGen Data
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -402,6 +410,8 @@ Test your trained policies:
    * SkillGen data generation and downstream policy success are sensitive to the task and the quality of dataset annotation, and can show high variance.
    * For cube stacking and bin cube stacking, data generation success is typically 40% to 70% when the dataset is properly annotated per the instructions.
    * Behavior Cloning (BC) policy success from 1000 generated demonstrations trained for 2000 epochs (default) is typically 40% to 85% for these tasks, depending on data quality.
+   * Training the policy with 1000 demonstrations and for 2000 epochs takes about 30 to 35 minutes on a RTX 6000 Ada GPU. Training time increases with the number of demonstrations and epochs.
+   * For dataset generation time, see :ref:`task-basic-cube-stacking` and :ref:`task-bin-cube-stacking`.
    * Recommendation: Train for the default 2000 epochs with about 1000 generated demonstrations, and evaluate multiple checkpoints saved after the 1000th epoch to select the best-performing policy.
 
 .. _cuRobo-interface-features: