File size: 2,999 Bytes
0037d53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | Verifying the UW Lab installation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To verify that the installation was successful, run the following command from the
top of the repository:
.. tab-set::
:sync-group: os
.. tab-item:: :icon:`fa-brands fa-linux` Linux
:sync: linux
.. code:: bash
# Option 1: Using the uwlab.sh executable
# note: this works for both the bundled python and the virtual environment
./uwlab.sh -p scripts/tutorials/00_sim/create_empty.py
# Option 2: Using python in your virtual environment
python scripts/tutorials/00_sim/create_empty.py
.. tab-item:: :icon:`fa-brands fa-windows` Windows
:sync: windows
.. code:: batch
:: Option 1: Using the uwlab.bat executable
:: note: this works for both the bundled python and the virtual environment
uwlab.bat -p scripts\tutorials\00_sim\create_empty.py
:: Option 2: Using python in your virtual environment
python scripts\tutorials\00_sim\create_empty.py
The above command should launch the simulator and display a window with a black
viewport. You can exit the script by pressing ``Ctrl+C`` on your terminal.
On Windows machines, please terminate the process from Command Prompt using
``Ctrl+Break`` or ``Ctrl+fn+B``.
.. figure:: /source/_static/setup/verify_install.jpg
:align: center
:figwidth: 100%
:alt: Simulator with a black window.
If you see this, then the installation was successful! |:tada:|
.. note::
If you see an error ``ModuleNotFoundError: No module named 'isaacsim'``, please ensure that the virtual
environment is activated and ``source _isaac_sim/setup_conda_env.sh`` has been executed (for uv as well).
Train a robot!
~~~~~~~~~~~~~~
You can now use UW Lab to train a robot through Reinforcement Learning! The quickest way to use UW Lab is through the predefined workflows using one of our **Batteries-included** robot tasks. Execute the following command to quickly train an ant to walk!
We recommend adding ``--headless`` for faster training.
.. tab-set::
:sync-group: os
.. tab-item:: :icon:`fa-brands fa-linux` Linux
:sync: linux
.. code:: bash
./uwlab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Ant-v0 --headless
.. tab-item:: :icon:`fa-brands fa-windows` Windows
:sync: windows
.. code:: batch
uwlab.bat -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Ant-v0 --headless
... Or a robot dog!
.. tab-set::
:sync-group: os
.. tab-item:: :icon:`fa-brands fa-linux` Linux
:sync: linux
.. code:: bash
./uwlab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 --headless
.. tab-item:: :icon:`fa-brands fa-windows` Windows
:sync: windows
.. code:: batch
uwlab.bat -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 --headless
|