Merge branch 'main' into rwiltz/add_arena_g1_locomanip_retargeting

Signed-off-by: Kelly Guo <kellyg@nvidia.com>

Author: kellyguo11

CONTRIBUTORS.md

@@ -36,6 +36,7 @@ Guidelines for modifications:
 * Pascal Roth
 * Sheikh Dawood
 * Ossama Ahmed
+* Greg Attra
 
 ## Contributors
 

docs/conf.py

@@ -157,6 +157,7 @@
     "omni.client",
     "omni.physx",
     "omni.physics",
+    "usdrt",
     "pxr.PhysxSchema",
     "pxr.PhysicsSchemaTools",
     "omni.replicator",

docs/source/_static/demos/bin_packing.jpg

@@ -24,6 +24,18 @@ teleoperation in Isaac Lab.
 
    See :ref:`manus-vive-handtracking` for more information on supported hand-tracking peripherals.
 
+.. note::
+
+   **Meta Quest 3 and Pico 4 Ultra Support (Early Access)**
+
+   Meta Quest 3 and Pico 4 Ultra are now supported via the `CloudXR Early Access program`_.
+   Join the program by mentioning isaac use cases. Once approved, you'll receive email to set up NGC,
+   then download `CloudXR.js with Isaac Teleop samples`_ and follow its guide.
+   Pico 4 Ultra must use HTTPS mode (see NGC documentation for details). General availability
+   will be provided in a future version of Isaac Lab.
+
+.. _`CloudXR Early Access program`: https://developer.nvidia.com/cloudxr-sdk-early-access-program/join
+.. _`CloudXR.js with Isaac Teleop samples`: https://catalog.ngc.nvidia.com/orgs/nvidia/resources/cloudxr-js-early-access?version=6.0.0-beta
 
 Overview
 --------

docs/source/how-to/cloudxr_teleoperation.rst

@@ -71,18 +71,26 @@ Training Performance
 --------------------
 
 We performed training with each RL library on the same ``Isaac-Humanoid-v0`` environment
-with ``--headless`` on a single RTX PRO 6000 GPU using 4096 environments
-and logged the total training time for 65.5M steps for each RL library.
-
+with ``--headless`` on a single NVIDIA GeForce RTX 4090 and logged the total training time
+for 65.5M steps (4096 environments x 32 rollout steps x 500 iterations).
 
 +--------------------+-----------------+
 | RL Library         | Time in seconds |
 +====================+=================+
-| RL-Games           | 207             |
+| RL-Games           | 201             |
 +--------------------+-----------------+
-| SKRL               | 208             |
+| SKRL               | 201             |
 +--------------------+-----------------+
-| RSL RL             | 199             |
+| RSL RL             | 198             |
 +--------------------+-----------------+
-| Stable-Baselines3  | 322             |
+| Stable-Baselines3  | 287             |
 +--------------------+-----------------+
+
+Training commands (check for the *'Training time: XXX seconds'* line in the terminal output):
+
+.. code:: bash
+
+    python scripts/reinforcement_learning/rl_games/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless
+    python scripts/reinforcement_learning/skrl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless
+    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless
+    python scripts/reinforcement_learning/sb3/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless

docs/source/overview/reinforcement-learning/rl_frameworks.rst

@@ -193,6 +193,30 @@ A few quick showroom scripts to run and checkout:
       :alt: Multiple assets managed through the same simulation handles
 
 
+-  Use the RigidObjectCollection spawn and view manipulation to demonstrate bin-packing example:
+
+   .. tab-set::
+      :sync-group: os
+
+      .. tab-item:: :icon:`fa-brands fa-linux` Linux
+         :sync: linux
+
+         .. code:: bash
+
+            ./isaaclab.sh -p scripts/demos/bin_packing.py
+
+      .. tab-item:: :icon:`fa-brands fa-windows` Windows
+         :sync: windows
+
+         .. code:: batch
+
+            isaaclab.bat -p scripts\demos\bin_packing.py
+
+   .. image:: ../_static/demos/bin_packing.jpg
+      :width: 100%
+      :alt: Spawning random number of random asset per env_id using combination of MultiAssetSpawner and RigidObjectCollection
+
+
 
 -  Use the interactive scene and spawn a simple parallel robot for pick and place:
 

docs/source/overview/showroom.rst

@@ -245,11 +245,10 @@
 import time
 import torch
 
-import isaacsim.core.utils.prims as prim_utils
 import psutil
-from isaacsim.core.utils.stage import create_new_stage
 
 import isaaclab.sim as sim_utils
+import isaaclab.sim.utils.prims as prim_utils
 from isaaclab.assets import RigidObject, RigidObjectCfg
 from isaaclab.scene.interactive_scene import InteractiveScene
 from isaaclab.sensors import (
@@ -261,6 +260,7 @@
     TiledCameraCfg,
     patterns,
 )
+from isaaclab.sim.utils.stage import create_new_stage
 from isaaclab.utils.math import orthogonalize_perspective_depth, unproject_depth
 
 from isaaclab_tasks.utils import load_cfg_from_registry

scripts/benchmarks/benchmark_cameras.py

@@ -35,9 +35,8 @@
 import numpy as np
 import torch
 
-import isaacsim.core.utils.prims as prim_utils
-
 import isaaclab.sim as sim_utils
+import isaaclab.sim.utils.prims as prim_utils
 from isaaclab.assets import Articulation
 from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR