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Update documentation to be compatible with PDF generation

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PiperOrigin-RevId: 822081040
Change-Id: I935b08751109e973398bc61c21f7217408983494
This commit is contained in:
Yuval Tassa
2025-10-21 06:01:43 -07:00
committed by Copybara-Service
parent 01d1ac34e0
commit 64add36adb
4 changed files with 28 additions and 20 deletions
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doc/XMLreference.rst
+19 -11
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@@ -16,21 +16,29 @@ XML schema
The table below summarizes the XML elements and their attributes in MJCF. Note that all information in MJCF is entered
through elements and attributes. Text content in elements is not used; if present, the parser ignores it.
.. collapse:: Collapse schema table
:open:
The symbols in the second column of the table have the following meaning:
.. only:: html
====== ===================================================
**!** required element, can appear only once
**?** optional element, can appear only once
**\*** optional element, can appear many times
**R** optional element, can appear many times recursively
====== ===================================================
.. collapse:: Collapse schema table
:open:
.. cssclass:: schema-small
The symbols in the second column of the table have the following meaning:
.. include:: XMLschema.rst
====== ===================================================
**!** required element, can appear only once
**?** optional element, can appear only once
**\*** optional element, can appear many times
**R** optional element, can appear many times recursively
====== ===================================================
.. cssclass:: schema-small
.. include:: XMLschema.rst
.. only:: latex
.. note::
The XML schema table is only available in the HTML version of this documentation.
.. _CType:
doc/changelog.rst
+5 -5
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@@ -319,7 +319,7 @@ Python bindings
12. Added support for nameless :ref:`mjSpec` objects in the ``bind`` method, see the corresponding
:ref:`section<PyMJCF>` in the documentation.
.. |mjspec_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |mjspec_colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mjspec.ipynb
Version 3.3.0 (Feb 26, 2025)
@@ -403,7 +403,7 @@ Python bindings
It is available here |rollout_colab|.
|br| Contribution by :github:user:`aftersomemath`.
.. |rollout_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |rollout_colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb
Version 3.2.7 (Jan 14, 2025)
@@ -924,7 +924,7 @@ Python bindings
Kinematics, is available here: |ls_colab|
|br| The video on the right shows example clips from the tutorial.
.. |ls_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |ls_colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb
@@ -1172,7 +1172,7 @@ New features
- The MJX API is compatible with MuJoCo but is missing some features in this release. See the outline of
:ref:`MJX feature parity <MjxFeatureParity>` for more details.
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb
.. youtube:: QewlEqIZi1o
@@ -1833,7 +1833,7 @@ General
notebook uses MuJoCo's native Python bindings, and includes a draft ``Renderer`` class, for easy rendering in Python.
|br| Try it yourself: |LQRopenincolab|
.. |LQRopenincolab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |LQRopenincolab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/deepmind/mujoco/blob/main/python/LQR.ipynb
#. Updates to humanoid model:
doc/mjx.rst
+1 -1
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@@ -36,7 +36,7 @@ Tutorial notebook
The following IPython notebook demonstrates the use of MJX along with reinforcement learning to train humanoid and
quadruped robots to locomote: |colab|.
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb
.. _MjxInstallation:
doc/python.rst
+3 -3
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@@ -26,7 +26,7 @@ Tutorial notebook
A MuJoCo tutorial using the Python bindings is available here: |mjcolab|
.. |mjcolab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |mjcolab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb
.. _PyInstallation:
@@ -796,7 +796,7 @@ values. The rollouts are run in parallel with an internally managed thread pool
thread) are passed as an argument. This notebook shows how to use ``rollout`` |rollout_colab|, along with some
benchmarks e.g., the figure below.
.. |rollout_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |rollout_colab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb
.. image:: images/python/rollout.png
@@ -868,7 +868,7 @@ This module contains optimization-related utilities.
The ``minimize.least_squares()`` function implements a nonlinear Least Squares optimizer solving sequential
Quadratic Programs with :ref:`mju_boxQP`. It is documented in the associated notebook: |lscolab|
.. |lscolab| image:: https://colab.research.google.com/assets/colab-badge.svg
.. |lscolab| image:: https://colab.research.google.com/assets/colab-badge.png
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb
.. _PyUSDexport: