Fixes some of the errors while building the docs (#3050)

# Description

Fixes most of the warnings and errors getting thrown while building the
docs locally. The ones from `isaaclab_mimic` still remain.

## Type of change

- Bug fix (non-breaking change which fixes an issue)
- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

---------

Signed-off-by: Mayank Mittal <[email protected]>
Co-authored-by: Kelly Guo <[email protected]>
Co-authored-by: Kelly Guo <[email protected]>
Co-authored-by: ooctipus <[email protected]>

Author: 3 people

docs/conf.py

@@ -182,6 +182,9 @@
     "pinocchio",
     "nvidia.srl",
     "flatdict",
+    "IPython",
+    "ipywidgets",
+    "mpl_toolkits",
 ]
 
 # List of zero or more Sphinx-specific warning categories to be squelched (i.e.,

docs/source/api/lab/isaaclab.actuators.rst

@@ -67,11 +67,6 @@ Ideal PD Actuator
 DC Motor Actuator
 -----------------
 
-.. figure:: ../../../_static/overview/actuator-group/dc_motor_clipping.jpg
-    :align: center
-    :figwidth: 100%
-    :alt: The effort clipping as a function of joint velocity for a linear DC Motor.
-
 .. autoclass:: DCMotor
   :members:
   :inherited-members:

docs/source/features/multi_gpu.rst

@@ -17,6 +17,7 @@ Multi-GPU Training
 ------------------
 
 Isaac Lab supports the following multi-GPU training frameworks:
+
 * `Torchrun <https://docs.pytorch.org/docs/stable/elastic/run.html>`_ through `PyTorch distributed <https://pytorch.org/docs/stable/distributed.html>`_
 * `JAX distributed <https://jax.readthedocs.io/en/latest/jax.distributed.html>`_
 
@@ -30,7 +31,7 @@ training. Torchrun manages the distributed training by:
 * **Script Execution**: Running the same training script (e.g., RL Games trainer) on each process.
 * **Environment Instances**: Each process creates its own instance of the Isaac Lab environment.
 * **Gradient Synchronization**: Aggregating gradients across all processes and broadcasting the synchronized
-gradients back to each process after each training step.
+  gradients back to each process after each training step.
 
 .. tip::
     Check out this `3 minute youtube video from PyTorch <https://www.youtube.com/watch?v=Cvdhwx-OBBo&list=PL_lsbAsL_o2CSuhUhJIiW0IkdT5C2wGWj&index=2>`_
@@ -48,9 +49,11 @@ module to manage the distributed training. When training with multiple GPUs usin
 * Each GPU runs an independent process
 * Each process executes the full training script
 * Each process maintains its own:
+
   * Isaac Lab environment instance (with *n* parallel environments)
   * Policy network copy
   * Experience buffer for rollout collection
+
 * All processes synchronize only for gradient updates
 
 For a deeper dive into how Torchrun works, checkout

docs/source/overview/teleop_imitation.rst

@@ -136,7 +136,7 @@ Pre-recorded demonstrations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 We provide a pre-recorded ``dataset.hdf5`` containing 10 human demonstrations for ``Isaac-Stack-Cube-Franka-IK-Rel-v0``
-`here <https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5/Isaac/IsaacLab/Mimic/dataset.hdf5>`_.
+`here <https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5/Isaac/IsaacLab/Mimic/dataset.hdf5>`__.
 This dataset may be downloaded and used in the remaining tutorial steps if you do not wish to collect your own demonstrations.
 
 .. note::
@@ -440,7 +440,7 @@ Generate the dataset
 ^^^^^^^^^^^^^^^^^^^^
 
 If you skipped the prior collection and annotation step, download the pre-recorded annotated dataset ``dataset_annotated_gr1.hdf5`` from
-`here <https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5/Isaac/IsaacLab/Mimic/dataset_annotated_gr1.hdf5>`_.
+`here <https://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/4.5/Isaac/IsaacLab/Mimic/dataset_annotated_gr1.hdf5>`__.
 Place the file under ``IsaacLab/datasets`` and run the following command to generate a new dataset with 1000 demonstrations.
 
 .. code:: bash

docs/source/setup/walkthrough/api_env_design.rst

@@ -82,7 +82,7 @@ Next, let's take a look at the contents of the other python file in our task dir
 
 .. code-block:: python
 
-  #imports
+  # imports
   .
   .
   .

docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst

@@ -20,7 +20,7 @@ the x axis, so we apply the ``root_link_quat_w`` to ``[1,0,0]`` to get the forwa
         observations = {"policy": obs}
         return observations
 
- So now what should the reward be?
+So now what should the reward be?
 
 When the robot is behaving as desired, it will be driving at full speed in the direction of the command. If we reward both
 "driving forward" and "alignment to the command", then maximizing that combined signal should result in driving to the command... right?

source/isaaclab/docs/CHANGELOG.rst

@@ -2,7 +2,7 @@ Changelog
 ---------
 
 0.41.4 (2025-07-30)
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~
 
 Fixed
 ^^^^^
@@ -15,7 +15,7 @@ Fixed
 
 
 0.41.3 (2025-07-30)
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~
 
 Added
 ^^^^^
@@ -33,8 +33,7 @@ Removed
 
 
 0.41.2 (2025-07-28)
-~~~~~~~~~~~~~~~~~~~~
-
+~~~~~~~~~~~~~~~~~~~
 Fixed
 ^^^^^
 
@@ -109,7 +108,7 @@ Fixed
 ^^^^^
 
 * Fixed :meth:`isaaclab.envs.mdp.events.reset_joints_by_scale`, :meth:`isaaclab.envs.mdp.events.reset_joints_by_offsets`
-restricting the resetting joint indices be that user defined joint indices.
+  restricting the resetting joint indices be that user defined joint indices.
 
 
 0.40.19 (2025-07-11)

source/isaaclab/isaaclab/actuators/actuator_pd.py

@@ -246,6 +246,12 @@ class DCMotor(IdealPDActuator):
     applied output torque will be driven to the Continuous Torque (`effort_limit`).
 
     The figure below demonstrates the clipping action for example (velocity, torque) pairs.
+
+    .. figure:: ../../_static/actuator-group/dc_motor_clipping.jpg
+        :align: center
+        :figwidth: 100%
+        :alt: The effort clipping as a function of joint velocity for a linear DC Motor.
+
     """
 
     cfg: DCMotorCfg

source/isaaclab/isaaclab/sim/simulation_cfg.py

@@ -94,7 +94,7 @@ class PhysxCfg:
 
         We recommend setting this flag to true only when the simulation step size is large (i.e., less than 30 Hz or more than 0.0333 seconds).
 
-    .. warn::
+    .. warning::
 
         Enabling this flag may lead to incorrect contact forces report from the contact sensor.
     """

source/isaaclab/test/sensors/test_contact_sensor.py

@@ -44,7 +44,7 @@ class ContactTestMode(Enum):
 
 
 @configclass
-class TestContactSensorRigidObjectCfg(RigidObjectCfg):
+class ContactSensorRigidObjectCfg(RigidObjectCfg):
     """Configuration for rigid objects used for the contact sensor test.
 
     This contains the expected values in the configuration to simplify test fixtures.
@@ -63,13 +63,13 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
     terrain: TerrainImporterCfg = MISSING
     """Terrain configuration within the scene."""
 
-    shape: TestContactSensorRigidObjectCfg = MISSING
+    shape: ContactSensorRigidObjectCfg = MISSING
     """RigidObject contact prim configuration."""
 
     contact_sensor: ContactSensorCfg = MISSING
     """Contact sensor configuration."""
 
-    shape_2: TestContactSensorRigidObjectCfg = None
+    shape_2: ContactSensorRigidObjectCfg = None
     """RigidObject contact prim configuration. Defaults to None, i.e. not included in the scene.
 
     This is a second prim used for testing contact filtering.
@@ -87,7 +87,7 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
 ##
 
 
-CUBE_CFG = TestContactSensorRigidObjectCfg(
+CUBE_CFG = ContactSensorRigidObjectCfg(
     prim_path="/World/Objects/Cube",
     spawn=sim_utils.CuboidCfg(
         size=(0.5, 0.5, 0.5),
@@ -106,7 +106,7 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
 )
 """Configuration of the cube prim."""
 
-SPHERE_CFG = TestContactSensorRigidObjectCfg(
+SPHERE_CFG = ContactSensorRigidObjectCfg(
     prim_path="/World/Objects/Sphere",
     spawn=sim_utils.SphereCfg(
         radius=0.25,
@@ -125,7 +125,7 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
 )
 """Configuration of the sphere prim."""
 
-CYLINDER_CFG = TestContactSensorRigidObjectCfg(
+CYLINDER_CFG = ContactSensorRigidObjectCfg(
     prim_path="/World/Objects/Cylinder",
     spawn=sim_utils.CylinderCfg(
         radius=0.5,
@@ -146,7 +146,7 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
 )
 """Configuration of the cylinder prim."""
 
-CAPSULE_CFG = TestContactSensorRigidObjectCfg(
+CAPSULE_CFG = ContactSensorRigidObjectCfg(
     prim_path="/World/Objects/Capsule",
     spawn=sim_utils.CapsuleCfg(
         radius=0.25,
@@ -167,7 +167,7 @@ class ContactSensorSceneCfg(InteractiveSceneCfg):
 )
 """Configuration of the capsule prim."""
 
-CONE_CFG = TestContactSensorRigidObjectCfg(
+CONE_CFG = ContactSensorRigidObjectCfg(
     prim_path="/World/Objects/Cone",
     spawn=sim_utils.ConeCfg(
         radius=0.5,
@@ -397,7 +397,7 @@ def test_sensor_print(setup_simulation):
 
 
 def _run_contact_sensor_test(
-    shape_cfg: TestContactSensorRigidObjectCfg,
+    shape_cfg: ContactSensorRigidObjectCfg,
     sim_dt: float,
     devices: list[str],
     terrains: list[TerrainImporterCfg],