Updates documentation (#471)

- Update Automator instructions
- Update URL links
- Update rsl_rl inference example
- Add tiled rendering memory note

docs/source/api/lab/omni.isaac.lab.app.rst

@@ -28,6 +28,12 @@ The following details the behavior of the class based on the environment variabl
   * ``LIVESTREAM=2`` enables streaming  via the `WebRTC Livestream`_ extension. This allows users to
     connect in a browser using the WebRTC protocol.
 
+  .. note::
+
+    Each Isaac Sim instance can only connect to one streaming client.
+    Connecting to an Isaac Sim instance that is currently serving a streaming client
+    results in an error for the second user.
+
 * **Enable cameras**: If the environment variable ``ENABLE_CAMERAS`` is set to 1, then the
   cameras are enabled. This is useful for running the simulator without a GUI but still rendering the
   viewport and camera images.

docs/source/features/actuators.rst

@@ -67,4 +67,4 @@ The following figure shows the actuator groups for a legged mobile manipulator:
 .. seealso::
 
     We provide implementations for various explicit actuator models. These are detailed in
-    `omni.isaac.lab.actuators <../api/lab.actuators.html>`_ sub-package.
+    `omni.isaac.lab.actuators <../api/lab/omni.isaac.lab.actuators.html>`_ sub-package.

docs/source/features/tiled_rendering.rst

@@ -11,6 +11,8 @@ Tiled Rendering
 
     This feature is only available from Isaac Sim version 4.0.0.
 
+    Tiled rendering requires heavy memory resources. We recommend running at most 256 cameras in the scene.
+
 Tiled rendering APIs provide a vectorized interface for collecting data from camera sensors.
 This is useful for reinforcement learning environments requiring vision in the loop.
 Tiled rendering works by concatenating camera outputs from multiple cameras and rendering

docs/source/setup/faq.rst

@@ -80,8 +80,8 @@ to Isaac Lab, please reach out to us.
 .. _PhysX: https://developer.nvidia.com/physx-sdk
 .. _Isaac Sim: https://developer.nvidia.com/isaac-sim
 .. _Isaac Gym: https://developer.nvidia.com/isaac-gym
-.. _IsaacGymEnvs: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
-.. _OmniIsaacGymEnvs: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs
+.. _IsaacGymEnvs: https://github.com/isaac-sim/IsaacGymEnvs
+.. _OmniIsaacGymEnvs: https://github.com/isaac-sim/OmniIsaacGymEnvs
 .. _Orbit: https://isaac-orbit.github.io/orbit
 .. _Isaac Automator: https://github.com/isaac-sim/IsaacAutomator
 .. _migration guides: ../migration/index.html

docs/source/setup/installation/cloud_installation.rst

@@ -10,6 +10,8 @@ The result is a fully configured remote desktop cloud workstation, which can be
 Installing Isaac Automator
 --------------------------
 
+For the most update-to-date and complete installation instructions, please refer to `Isaac Automator <https://github.com/isaac-sim/IsaacAutomator?tab=readme-ov-file#installation>`__.
+
 To use Isaac Automator, first clone the repo:
 
 .. code-block:: bash
@@ -49,9 +51,31 @@ Running Isaac Automator
 
 To run Isaac Automator, first build the Isaac Automator container:
 
-.. code-block:: bash
+.. tabs::
+
+  .. tab:: Linux
+      .. code-block:: bash
+
+         ./build
+
+  .. tab:: Windows
+      .. code-block:: bash
+
+         docker build --platform linux/x86_64 -t isa .
+
+Next, enter the automator container:
+
+.. tabs::
+
+  .. tab:: Linux
+      .. code-block:: bash
+
+          ./run
+
+  .. tab:: Windows
+      .. code-block:: bash
 
-   ./build
+          docker run --platform linux/x86_64 -it --rm -v .:/app isa bash
 
 Next, run the deployed script for your preferred cloud:
 

docs/source/setup/sample.rst

@@ -215,7 +215,7 @@ from the environments into the respective libraries function argument and return
       # run script for training
       ./isaaclab.sh -p source/standalone/workflows/rsl_rl/train.py --task Isaac-Reach-Franka-v0 --headless
       # run script for playing with 32 environments
-      ./isaaclab.sh -p source/standalone/workflows/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth
+      ./isaaclab.sh -p source/standalone/workflows/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint model.pt
 
 All the scripts above log the training progress to `Tensorboard`_ in the ``logs`` directory in the root of
 the repository. The logs directory follows the pattern ``logs/<library>/<task>/<date-time>``, where ``<library>``