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17 changes: 12 additions & 5 deletions docs/source/setup/tutorial.rst
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Tutorial
========

.. figure:: https://download.isaacsim.omniverse.nvidia.com/isaaclab/images/tutorial_so101_vialplace_play.gif
:alt: The SO-101 arm picking up a vial and placing it in a rack.
:align: center
:width: 85%

.. note::

This tutorial ties in with the `NVIDIA Sim-to-Real SO-101 learning course
<https://docs.nvidia.com/learning/physical-ai/sim-to-real-so-101/latest/index.html>`__ and its
`workshop repository <https://github.com/isaac-sim/Sim-to-Real-SO-101-Workshop>`__, which present a broader
end-to-end physical AI workflow with the SO-101, Isaac Lab, and NVIDIA Isaac GR00T.

This tutorial builds a complete robot-learning workflow around an `SO-101 arm
<https://github.com/TheRobotStudio/SO-ARM100>`__ that picks up a vial and places it in a rack.
Instead of assembling disconnected examples, you will work with a production-shaped downstream project:
`IsaacLabTutorial main branch <https://github.com/isaac-sim/IsaacLabTutorial/tree/main>`__. The project includes the
robot and workshop assets, a manager-based reinforcement-learning environment, state and wrist-camera observations,
RSL-RL agent configurations, tests, state-policy training, and state-to-vision policy distillation.

.. image:: https://raw.githubusercontent.com/isaac-sim/IsaacLabTutorial/main/media/demo.gif
:alt: The SO-101 arm picking up a vial and placing it in a rack.
:align: center
:width: 100%

By the end, you will know how a downstream Isaac Lab task is packaged, discovered, validated, trained,
and evaluated. You will also know where to change the robot, scene, MDP, and learning configuration for
your own task.
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