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docs: headless doc cleanup #6108

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4 changes: 2 additions & 2 deletions docs/source/features/hydra.rst
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Expand Up @@ -48,8 +48,8 @@ As a result, training with hydra arguments can be run with the following syntax:

./isaaclab.sh train --rl_library sb3 --task=Isaac-Cartpole env.actions.joint_effort.scale=10.0 agent.seed=2024

The above command will run training with the task ``Isaac-Cartpole`` in headless mode, and set the
``env.actions.joint_effort.scale`` parameter to 10.0 and the ``agent.seed`` parameter to 2024.
The above command will run training with the task ``Isaac-Cartpole`` without selecting a visualizer,
and set the ``env.actions.joint_effort.scale`` parameter to 10.0 and the ``agent.seed`` parameter to 2024.

.. note::

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2 changes: 1 addition & 1 deletion docs/source/how-to/save_camera_output.rst
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Expand Up @@ -94,7 +94,7 @@ To run the accompanying script, execute the following command:
# Usage with saving and drawing
python scripts/tutorials/04_sensors/run_usd_camera.py --save --draw --enable_cameras

# Usage with saving only in headless mode
# Usage with saving only (no visualizer)
python scripts/tutorials/04_sensors/run_usd_camera.py --save --enable_cameras


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2 changes: 1 addition & 1 deletion docs/source/overview/core-concepts/physical-backends/ovphysx/index.rst
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Expand Up @@ -112,7 +112,7 @@ syntax as the other backends:

./isaaclab.sh -p scripts/environments/zero_agent.py --task Isaac-Cartpole-Direct --num_envs 128 presets=ovphysx

This command starts a headless zero-action rollout; stop it with ``Ctrl+C``
This command starts a zero-action rollout without selecting a visualizer; stop it with ``Ctrl+C``
after the environment has started and stepped successfully.

Status and follow-up
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6 changes: 3 additions & 3 deletions docs/source/policy_deployment/00_hover/hover_policy.rst
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Expand Up @@ -59,7 +59,7 @@ Execute the following command from the ``HOVER`` directory to train the teacher

${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/train_teacher_policy.py \
--num_envs 1024 \
--reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \
--reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl

The teacher policy is trained for 10000000 iterations, or until the user interrupts the training.
The resulting checkpoint is stored in ``neural_wbc/data/data/policy/h1:teacher/`` and the filename is ``model_<iteration_number>.pt``.
Expand All @@ -74,7 +74,7 @@ Execute the following command from the ``HOVER`` directory to train the student
--num_envs 1024 \
--reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \
--teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \
--teacher_policy.checkpoint model_<iteration_number>.pt \
--teacher_policy.checkpoint model_<iteration_number>.pt

This assumes that you have already trained the teacher policy as there is no provided teacher policy in the repo.

Expand Down Expand Up @@ -167,7 +167,7 @@ To deploy the trained policy on the H1 robot,
--reference_motion_path neural_wbc/data/data/motions/<motion_name>.pkl \
--robot unitree_h1 \
--max_iterations 5000 \
--num_envs 1 \
--num_envs 1

.. note::

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