Cherry-picks RLinf/compass doc updates, visualizer fixes, presets and imports fixes, Newton 1.2.1rc2 pin

.github/workflows/check-links.yml

@@ -104,6 +104,12 @@ jobs:
           --exclude 'docs\.ray\.io'
           --exclude 'docs\.conda\.io'
           --exclude 'stackoverflow\.com'
+          --exclude 'docs\.isaacsim\.omniverse\.nvidia\.com'
+          --exclude 'download\.isaacsim\.omniverse\.nvidia\.com/isaaclab/images'
+          --exclude 'dx\.doi\.org/10\.1109/JRA\.1987\.1087068'
+          --exclude 'andrew\.cmu\.edu/course/10-703/textbook/BartoSutton\.pdf'
+          --exclude 'www\.nvidia\.com/en-us/security'
+          --exclude 'bostondynamics\.com/reinforcement-learning-researcher-kit'
           --max-retries 5
           --retry-wait-time 10
           --timeout 20

docs/index.rst

@@ -130,7 +130,6 @@ Table of Contents
    source/features/hydra
    source/features/multi_gpu
    source/features/population_based_training
-   Tiled Rendering</source/overview/core-concepts/sensors/camera>
    source/features/ray
    source/features/reproducibility
 

docs/source/how-to/index.rst

@@ -101,6 +101,17 @@ Isaac Lab.
     draw_markers
 
 
+Using Visualizers
+-----------------
+
+This guide demonstrates how to use visualizer-specific views for monitoring and debugging Isaac Lab environments.
+
+.. toctree::
+    :maxdepth: 1
+
+    visualizer_tiled_camera
+
+
 Working with Simulation Data
 ----------------------------
 

docs/source/how-to/optimize_stage_creation.rst

@@ -49,7 +49,7 @@ Stage in memory can be toggled by setting the :attr:`isaaclab.sim.SimulationCfg.
     env = ManagerBasedRLEnv(cfg=cfg)
 
 When using stage in memory without an existing RL environment class, wrap the stage creation steps
-in a :py:keyword:`with` statement to set the stage context. The stage is automatically attached
+in a ``with`` statement to set the stage context. The stage is automatically attached
 to the USD context when ``SimulationContext`` is created with ``create_stage_in_memory=True``.
 
 **Using Stage in Memory with a manual scene setup**

docs/source/how-to/visualizer_tiled_camera.rst

@@ -0,0 +1,159 @@
+.. _how-to-visualizer-tiled-camera:
+
+Using Visualizer Tiled Cameras
+==============================
+
+.. currentmodule:: isaaclab
+
+For general visualizer documentation, see :doc:`/source/overview/core-concepts/visualization`.
+
+The visualizer tiled camera view is a live monitoring and debugging tool. It opens a
+non-interactive panel in the Kit or Newton visualizer and displays tiled camera views
+across all selected environments. They can stream observation camera data or generate cameras
+that follow the robots.
+
+This guide is accompanied by the ``run_tiled_camera_visualizer.py`` script in the
+``IsaacLab/scripts/tutorials/07_visualizers`` directory.
+
+Running this script demonstrates two ways to use tiled cameras:
+
+- configured tiled cameras pointed at and following moving Anymal-D robots shown in the Kit visualizer
+- streaming from existing wrist-mounted robot cameras shown in the Newton visualizer
+
+Note: Visualizer tiled cameras are currently supported only in the Kit and Newton visualizers.
+Either visualizer can be used to run either example.
+
+.. dropdown:: Code for run_tiled_camera_visualizer.py
+   :icon: code
+
+   .. literalinclude:: ../../../scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py
+      :language: python
+      :emphasize-lines: 72-78,87-94
+      :linenos:
+
+
+Example One: Following Anymal-D Robots
+--------------------------------------
+
+The Kit Visualizer shows the tiled camera view in a separate tab inside the main
+Viewport window. The highlighted tab area in the figures below shows where to
+toggle between the interactive viewport and the visualizer tiled camera view.
+
+.. figure:: ../_static/visualizers/kit_viz_anymal_iteractive_view.jpg
+   :width: 100%
+   :alt: Kit visualizer interactive viewport for Anymal-D robots
+
+   Kit visualizer showing the default interactive viewport.
+
+.. figure:: ../_static/visualizers/kit_viz_anymal_tiled_view.jpg
+   :width: 100%
+   :alt: Kit visualizer tiled camera view for Anymal-D robots
+
+   Kit visualizer showing the tiled camera view generated for selected Anymal-D
+   robots.
+
+Note, you can also display the main visualizer camera and the tiled camera view side by
+side for dual monitoring.
+
+To run the tutorial with the args for this example, use:
+
+.. code-block:: bash
+
+   ./isaaclab.sh -p scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
+     --enable_cameras \
+     --task Isaac-Velocity-Rough-Anymal-D-v0 \
+     --num_envs 256 \
+     --viz kit
+
+Within the script, you’ll find the ``KitVisualizerCfg`` configuration used to
+generate this example. You can use this config as a template for your own use
+cases.
+
+In this example, a set of cameras is created to point toward each robot's base
+prim and follow its motion. The camera's position, relative to the prim, is set
+by the ``tiled_cam_eye`` field of ``KitVisualizerCfg``. For this demo, the
+camera is offset by ``(3.0, 3.0, 3.0)`` from each robot base. If you change ``tiled_cam_eye``
+(for example, to ``(0, 0, 5)``), the panel will show a top-down view instead.
+
+In this example, there are 256 total environments, and we randomly sample 36 to stream to the
+tiled camera view.
+
+Also note that the Kit visualizer tiled camera view requires passing the
+``--enable_cameras`` CLI arg.
+
+
+Example Two: Streaming from Robot-Mounted Cameras
+-------------------------------------------------
+
+The Newton visualizer provides a tiled camera view in a lightweight OpenGL window.
+Use the highlighted ``Tiled Camera View`` dropdown in the left-hand sidebar to
+show or hide the tiled camera panel.
+
+.. figure:: ../_static/visualizers/newton_viz_galbot_interactive_view.jpg
+   :width: 100%
+   :alt: Newton visualizer interactive view for the Galbot cube stacking environment
+
+   Newton visualizer showing the default interactive viewport.
+
+.. figure:: ../_static/visualizers/newton_viz_galbot_tiled_view.jpg
+   :width: 100%
+   :alt: Newton visualizer tiled camera view for Galbot wrist cameras
+
+   Newton visualizer showing the selected Galbot head-camera feeds in the tiled
+   camera panel.
+
+In this example, we use the Galbot cube stacking environment, which comes with
+built-in wrist-mounted cameras. This setup provides an egocentric view of the
+gripper, table, and cubes in each selected environment.
+
+To launch this example, run:
+
+.. code-block:: bash
+
+   ./isaaclab.sh -p scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \
+     --task Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0 \
+     --num_envs 25 \
+     --viz newton
+
+Within the script, the ``NewtonVisualizerCfg`` is configured to stream images from the
+existing camera sensor located at
+``/World/envs/env_.*/Robot/head_camera_sim_view_frame/head_camera``. This path
+points to the head camera, but you can edit the ``tiled_cam_prim_path``
+field of ``NewtonVisualizerCfg`` in the script to show a different existing camera if
+needed.
+
+In this demo, 25 environments are simulated, and 12 camera feeds are shown in the tiled panel by default.
+
+
+Configuration notes
+-------------------
+
+To customize tiled camera behavior, edit the highlighted ``VisualizerCfg`` fields in
+``run_tiled_camera_visualizer.py``:
+
+* For generated cameras, ``tiled_cam_target_prim_path`` chooses the followed prim and
+  ``tiled_cam_eye`` sets the camera offset from that prim.
+* For existing scene cameras, ``tiled_cam_prim_path`` must match an Isaac Lab
+  :class:`~isaaclab.sensors.Camera` sensor in the selected task.
+* ``tiled_cam_num`` controls how many environment tiles are shown.
+
+
+Troubleshooting
+---------------
+
+* If a generated view fails with a missing prim error, check that
+  ``tiled_cam_target_prim_path`` resolves in each selected environment. Common template
+  forms include ``/World/envs/*/...`` and ``/World/envs/env_.*/...``.
+* If an existing-camera view reports that no Isaac Lab camera owns the prim, check that
+  ``tiled_cam_prim_path`` matches a :class:`~isaaclab.sensors.Camera` sensor in the task.
+* If ``rerun`` or ``viser`` is selected, use ``--viz kit`` or ``--viz newton`` instead.
+  The tiled camera panel is currently implemented for Kit and Newton.
+* If the view is too expensive, reduce ``tiled_cam_num``, ``--num_envs``, or the camera
+  resolution. The visualizer caps the tiled panel at 100 tiles.
+
+
+See also
+--------
+
+* :doc:`/source/overview/core-concepts/visualization` - visualizer configuration and UI controls.
+* :doc:`/source/how-to/configure_rendering` - selecting rendering presets and quality modes.

docs/source/overview/core-concepts/visualization.rst

@@ -200,6 +200,9 @@ Camera Modes
 To configure camera modes, including launching a tiled camera view, edit the fields described below in the
 ``VisualizerCfg`` config class.
 
+For runnable Kit and Newton examples that use generated and existing tiled cameras,
+see :doc:`/source/how-to/visualizer_tiled_camera`.
+
 The default visualizer camera mode is interactive, with ``eye`` and ``lookat`` specifying the initial pose.
 Kit and Newton visualizers can also run additional tiled camera image panels.
 
@@ -219,11 +222,16 @@ Note, Kit tiled camera views require launching with ``--enable_cameras``.
      - ``tiled_cam_view=False``, ``eye=(4, -4, 3)``, ``lookat=(0, 0, 0)``
      - Interactive visualizer camera starts at ``eye`` and looks at the fixed ``lookat`` coordinate.
    * - Generated tiled camera
-     - ``tiled_cam_view=True``, ``tiled_cam_prim_path=None``, ``tiled_cam_target_prim_path="/World/envs/*/Robot/base"``
+     - ``tiled_cam_view=True``, ``tiled_cam_prim_path=None``, ``tiled_cam_target_prim_path="/World/envs/*/Robot"``
      - The visualizer creates per-env cameras. Each camera looks at the matched target prim, with ``tiled_cam_eye`` as an offset from that target.
+       Note that the ``tiled_cam_target_prim_path`` has a default value, but different environments may require different paths.
    * - Existing tiled camera sensors
      - ``tiled_cam_view=True``, ``tiled_cam_prim_path="/World/envs/*/Camera"``
-     - The visualizer displays existing Isaac Lab ``Camera`` sensor output. Generated-camera fields such as ``tiled_cam_eye`` and ``tiled_cam_target_prim_path`` are ignored.
+     - The visualizer displays existing Isaac Lab ``Camera`` sensor output. Generated-camera fields such as ``tiled_cam_eye`` and
+       ``tiled_cam_target_prim_path`` are ignored. Note that the ``tiled_cam_prim_path`` has a default value, but different
+       environments may require different paths. This mode requires an environment that registers Isaac Lab ``Camera`` sensors
+       in ``scene.sensors``. For Cartpole, use a camera task such as ``Isaac-Cartpole-Camera``. The plain ``Isaac-Cartpole``
+       task has no ``/World/envs/*/Camera`` sensor, so leave ``tiled_cam_prim_path=None`` to use generated visualizer cameras.
 
 **How to Access the Tiled Camera View in the UI**
 
@@ -416,8 +424,8 @@ Newton Visualizer
         tiled_cam_prim_path=None,                 # Existing Camera sensor prim path, e.g. "/World/envs/*/Camera"
         tiled_cam_eye=(4.0, -4.0, 3.0),           # Eye offset for generated tiled cameras
         tiled_cam_target_prim_path=(              # Prim that generated cameras follow/look at
-            "/World/envs/*/Robot/base"
-        ),
+            "/World/envs/*/Robot"                 # This is the default value, but different environments
+        ),                                        # may require a different paths.
 
         # Performance tuning
         update_frequency=1,                       # Update every N frames (1=every frame)
@@ -603,6 +611,13 @@ The FPS control in the Rerun visualizer UI may not affect the visualization fram
 Currently, live plots are only available in the Kit Visualizer.
 
 
+**Newton Contact Visualization**
+
+Newton's native ``Show Contacts`` view can show all contacts from the Newton physics contact buffer. When running
+with PhysX, the Newton visualizer can only show contacts reported by configured Isaac Lab contact sensors, so
+currently the set of displayed contacts may differ across backends.
+
+
 **Viser Visualizer Renderer Requirement**
 
 The Viser visualizer requires a Newton model, which is provided automatically by

docs/source/policy_deployment/00_hover/hover_policy.rst

@@ -191,4 +191,4 @@ To deploy the trained policy on the H1 robot,
 .. _README of Sim2Real deployment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md
 .. _Hardware Environment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md
 .. _Mujoco Environment: https://github.com/NVlabs/HOVER/tree/main/neural_wbc/mujoco_wrapper
-.. _Unitree H1 Robot: https://unitree.com/h1
+.. _Unitree H1 Robot: https://www.unitree.com/h1

docs/source/policy_deployment/03_compass_with_NuRec/compass_navigation_policy_with_NuRec.rst

@@ -369,7 +369,7 @@ Where:
 - ``--environment nova_carter-galileo``: Specifies the NuRec Real2Sim Galileo environment
 
 The training will run for the number of iterations specified in the config file (default: 1000 iterations).
-The resulting checkpoint will be stored in ``<output_dir>/checkpoints/`` with the filename ``model_<iteration_number>.pt``.
+Checkpoints are saved directly under ``<output_dir>/`` with the filename ``model_<iteration_number>.pt`` (e.g., ``model_0.pt``, ``model_50.pt``, ``model_100.pt``), written every 50 iterations.
 Videos will be saved in ``<output_dir>/videos/``.
 
 .. note::
@@ -441,7 +441,7 @@ Execute the following command from the ``COMPASS`` directory to evaluate the tra
 
 Where:
 
-- ``<path/to/residual_policy_ckpt>``: Path to the trained residual policy checkpoint (e.g., ``<output_dir>/checkpoints/model_1000.pt``)
+- ``<path/to/residual_policy_ckpt>``: Path to the trained residual policy checkpoint (e.g., ``<output_dir>/model_1000.pt``)
 - ``--video``: Enable video recording during evaluation
 - ``--video_interval``: Record video every N iterations
 

docs/source/policy_deployment/05_leapp/exporting_policies_with_leapp.rst

@@ -4,7 +4,7 @@ Exporting Policies with LEAPP
 .. currentmodule:: isaaclab
 
 This guide covers how to export trained reinforcement learning policies from Isaac Lab using
-`LEAPP <https://github.com/nvidia-isaac/leapp>`_ (Lightweight Export Annotations for Policy Pipelines).
+`LEAPP <https://github.com/nvidia-isaac/leapp>`__ (Lightweight Export Annotations for Policy Pipelines).
 The main goal of the LEAPP export path is to package the policy together with the input and
 output semantics needed for deployment, so downstream users do not need to reimplement Isaac Lab
 observation preprocessing, action postprocessing, or recurrent-state handling by hand.
@@ -64,7 +64,7 @@ consumers can run the policy without reconstructing observation ordering, comman
 targets, or policy feedback loops themselves.
 
 For a detailed description of LEAPP's generated artifacts and APIs, refer to the
-`LEAPP documentation <https://github.com/nvidia-isaac/leapp/tree/main/docs>`_.
+`LEAPP documentation <https://github.com/nvidia-isaac/leapp/tree/main/docs>`__.
 
 
 Exporting a Policy
@@ -289,6 +289,6 @@ workflow policies are not currently supported by ``scripts/reinforcement_learnin
 Further Reading
 ---------------
 
-- `LEAPP documentation <https://github.com/nvidia-isaac/leapp/tree/main/docs>`_
-- `LEAPP API reference <https://github.com/nvidia-isaac/leapp/blob/main/docs/api.md>`_
+- `LEAPP documentation <https://nvidia-isaac.github.io/leapp/>`__
+- `LEAPP API reference <https://nvidia-isaac.github.io/leapp/api/index.html>`__
 - :class:`~envs.LeappDeploymentEnv` API reference

docs/source/refs/migration.rst

@@ -157,7 +157,7 @@ Two key patterns support this:
    initialized.
 
 For full details, examples, and the ``{DIR}`` placeholder convention, see the
-:ref:`contributing` guide — in particular the
+:doc:`contributing` guide — in particular the
 `Lazy Loading & Module Exports <contributing.html#lazy-loading-module-exports>`__,
 `Resolvable Strings <contributing.html#resolvable-strings>`__, and
 `Config + Implementation File Split <contributing.html#config-implementation-file-split>`__
@@ -197,7 +197,7 @@ With this in place, ``import my_package`` will not eagerly import any submodules
 are loaded on first access, giving ``SimulationApp`` time to initialize and auto-detect the
 correct backend.
 
-For more details, refer to the :ref:`contributing` guide.
+For more details, refer to the :doc:`contributing` guide.
 
 
 .. _simple script: https://gist.github.com/kellyguo11/3e8f73f739b1c013b1069ad372277a85

docs/source/tutorials/00_sim/launch_app.rst

@@ -178,5 +178,5 @@ For more details on headless mode and launching visualizers, see
 :doc:`/source/migration/migrating_to_isaaclab_3-0`.
 
 
-.. _specification: https://docs.isaacsim.omniverse.nvidia.com/latest/py/source/extensions/isaacsim.simulation_app/docs/index.html#isaacsim.simulation_app.SimulationApp.DEFAULT_LAUNCHER_CONFIG
+.. _specification: https://docs.isaacsim.omniverse.nvidia.com/latest/py/source/extensions/isaacsim.simulation_app/docs/api.html#isaacsim.simulation_app.SimulationApp.DEFAULT_LAUNCHER_CONFIG
 .. _WebRTC Livestreaming: https://docs.isaacsim.omniverse.nvidia.com/latest/installation/manual_livestream_clients.html#isaac-sim-short-webrtc-streaming-client

docs/source/tutorials/06_exporting/exporting_direct_workflow_policies_with_leapp.rst

@@ -236,6 +236,6 @@ into deployment systems.
 .. note::
 
    Refer to the `LEAPP semantic annotation guide
-   <https://github.com/nvidia-isaac/leapp/blob/main/docs/5_semantic_data_annotation.md>`_
-   and `LEAPP API reference <https://github.com/nvidia-isaac/leapp/blob/main/docs/api.md>`_
+   <https://nvidia-isaac.github.io/leapp/guides/semantics.html>`_
+   and `LEAPP API reference <https://nvidia-isaac.github.io/leapp/api/index.html>`_
    for details on authoring semantic annotations.

scripts/benchmarks/benchmark_non_rl.py

@@ -15,7 +15,7 @@
 
 from isaaclab.app import AppLauncher
 
-from isaaclab_tasks.utils import fold_preset_tokens, setup_preset_cli
+from isaaclab_tasks.utils import setup_preset_cli
 
 # add argparse arguments
 parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.")
@@ -50,7 +50,6 @@
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
 args_cli, hydra_args = setup_preset_cli(parser)
-hydra_args = fold_preset_tokens(hydra_args)
 sys.argv = [sys.argv[0]] + hydra_args
 if args_cli.video:
     args_cli.enable_cameras = True

scripts/benchmarks/benchmark_rlgames.py

@@ -15,7 +15,7 @@
 
 from isaaclab.app import AppLauncher
 
-from isaaclab_tasks.utils import fold_preset_tokens, setup_preset_cli
+from isaaclab_tasks.utils import setup_preset_cli
 
 from scripts.benchmarks.early_stop import (
     RlGamesEarlyStopObserver,
@@ -67,7 +67,6 @@
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
 args_cli, hydra_args = setup_preset_cli(parser)
-hydra_args = fold_preset_tokens(hydra_args)
 sys.argv = [sys.argv[0]] + hydra_args
 if args_cli.video:
     args_cli.enable_cameras = True

scripts/benchmarks/benchmark_rsl_rl.py

@@ -15,7 +15,7 @@
 
 from isaaclab.app import AppLauncher
 
-from isaaclab_tasks.utils import fold_preset_tokens, setup_preset_cli
+from isaaclab_tasks.utils import setup_preset_cli
 
 from scripts.benchmarks.early_stop import (
     RslRlEarlyStopWrapper,
@@ -72,7 +72,6 @@
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
 args_cli, hydra_args = setup_preset_cli(parser)
-hydra_args = fold_preset_tokens(hydra_args)
 sys.argv = [sys.argv[0]] + hydra_args
 if args_cli.video:
     args_cli.enable_cameras = True