Update documentation to be compatible with PDF generation

PiperOrigin-RevId: 822081040
Change-Id: I935b08751109e973398bc61c21f7217408983494

doc/XMLreference.rst

@@ -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
 
-   ====== ===================================================
+   .. collapse:: Collapse schema table
-   **!**  required element, can appear only once
+      :open:
-   **?**  optional element, can appear only once
-   **\*** optional element, can appear many times
-   **R**  optional element, can appear many times recursively
-   ====== ===================================================
 
-   .. 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

@@ -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

@@ -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

@@ -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: