Adds camera to G1 Steering Wheel environment

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

@@ -636,32 +636,33 @@ To create a comprehensive locomanipulation dataset that combines both manipulati
 
    G1 humanoid robot performing locomanipulation with navigation capabilities.
 
-The navigation dataset generation process takes the previously generated manipulation dataset and creates scenarios where the robot must navigate from one location to another while performing manipulation tasks. This creates a more complex dataset that includes both locomotion and manipulation behaviors.
+The locomanipulation dataset generation process takes the previously generated manipulation dataset and creates scenarios where the robot must navigate from one location to another while performing manipulation tasks. This creates a more complex dataset that includes both locomotion and manipulation behaviors.
 
-To generate the navigation dataset, use the following command:
+To generate the locomanipulation dataset, use the following command:
 
 .. code:: bash
 
    ./isaaclab.sh -p \
-       scripts/imitation_learning/disjoint_navigation/generate_navigation.py \
+       scripts/imitation_learning/locomanipulation_sdg/generate_data.py \
        --device cpu \
        --kit_args="--enable isaacsim.replicator.mobility_gen" \
-       --task="Isaac-G1-Disjoint-Navigation" \
+       --task="Isaac-G1-SteeringWheel-Locomanipulation" \
        --dataset ./datasets/generated_dataset_g1_locomanip.hdf5 \
        --num_runs 1 \
        --lift_step 70 \
        --navigate_step 120 \
        --enable_pinocchio \
-       --output_file ./datasets/generated_dataset_g1_navigation.hdf5
+       --output_file ./datasets/generated_dataset_g1_locomanipulation_sdg.hdf5 \
+       --enable_cameras
 
 .. note::
 
    The input dataset (``--dataset``) should be the manipulation dataset generated in the previous step. You can specify any output filename using the ``--output_file_name`` parameter.
 
-The key parameters for navigation dataset generation are:
+The key parameters for locomanipulation dataset generation are:
 
-* ``--lift_step 70``: Number of steps for the lifting phase of the manipulation task
-* ``--navigate_step 120``: Number of steps for the navigation phase between locations
+* ``--lift_step 70``: Number of steps for the lifting phase of the manipulation task.  This should mark the point immediately after the robot has grasped the object.
+* ``--navigate_step 120``: Number of steps for the navigation phase between locations.  This should make the point where the robot has lifted the object and is ready to walk.
 * ``--output_file``: Name of the output dataset file
 
 This process creates a dataset where the robot performs the manipulation task at different locations, requiring it to navigate between points while maintaining the learned manipulation behaviors. The resulting dataset can be used to train policies that combine both locomotion and manipulation capabilities.
@@ -672,7 +673,22 @@ This process creates a dataset where the robot performs the manipulation task at
 
    .. code:: bash
 
-      ./isaaclab.sh -p scripts/imitation_learning/disjoint_navigation/plot_navigation_trajectory.py --input_file datasets/generated_dataset_g1_navigation.hdf5 --output_dir /PATH/TO/DESIRED_OUTPUT_DIR
+      ./isaaclab.sh -p scripts/imitation_learning/locomanipulation_sdg/plot_navigation_trajectory.py --input_file datasets/generated_dataset_g1_locomanipulation_sdg.hdf5 --output_dir /PATH/TO/DESIRED_OUTPUT_DIR
+
+The data generated from this locomanipulation pipeline can also be used to finetune an imitation learning policy using GR00T N1.5.  To do this,
+you may convert the generated dataset to LeRobot format as expected by GR00T N1.5, and then run the finetuning script provided
+in the GR00T N1.5 repository.  An example closed-loop policy rollout is shown in the video below:
+
+.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/locomanipulation_sdg_disjoint_nav_groot_policy_4x.gif
+   :width: 100%
+   :align: center
+   :alt: Simulation rollout of GR00T N1.5 policy finetuned for locomanipulation
+   :figclass: align-center
+
+   Simulation rollout of GR00T N1.5 policy finetuned for locomanipulation.
+
+The policy shown above uses the camera image, hand poses, hand joint positions, object pose, and base goal pose as inputs.
+The output of the model is the target base velocity, hand poses, and hand joint positions for the next several timesteps.
 
 
 Demo 3: Visuomotor Policy for a Humanoid Robot