GTLIDAR · fei-yang-wu · Jul 8, 2025 · Jun 17, 2025 · Jun 17, 2025 · Jun 23, 2025
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
@@ -40,11 +40,13 @@ Guidelines for modifications:
 * Amr Mousa
 * Andrej Orsula
 * Anton Bjørndahl Mortensen
+* Antonin Raffin
 * Arjun Bhardwaj
 * Ashwin Varghese Kuruttukulam
 * Bikram Pandit
 * Bingjie Tang
 * Brayden Zhang
+* Brian Bingham
 * Cameron Upright
 * Calvin Yu
 * Cheng-Rong Lai
@@ -74,6 +76,7 @@ Guidelines for modifications:
 * Jinqi Wei
 * Johnson Sun
 * Kaixi Bao
+* Kris Wilson
 * Kourosh Darvish
 * Kousheek Chakraborty
 * Lionel Gulich
@@ -86,6 +89,7 @@ Guidelines for modifications:
 * Miguel Alonso Jr
 * Muhong Guo
 * Nicola Loi
+* Norbert Cygiert
 * Nuoyan Chen (Alvin)
 * Nuralem Abizov
 * Ori Gadot
@@ -102,6 +106,7 @@ Guidelines for modifications:
 * Rosario Scalise
 * Ryley McCarroll
 * Shafeef Omar
+* Shaoshu Su
 * Shundo Kishi
 * Stefan Van de Mosselaer
 * Stephan Pleines
@@ -119,6 +124,7 @@ Guidelines for modifications:
 * Zhengyu Zhang
 * Ziqi Fan
 * Zoe McCarthy
+* David Leon
 
 ## Acknowledgements
 

diff --git a/README.md b/README.md
@@ -93,12 +93,13 @@ or opening a question on its [forums](https://forums.developer.nvidia.com/c/agx-
 
 ## Connect with the NVIDIA Omniverse Community
 
-Have a project or resource you'd like to share more widely? We'd love to hear from you! Reach out to the
-NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to discuss potential opportunities
-for broader dissemination of your work.
+Do you have a project or resource you'd like to share more widely? We'd love to hear from you!
+Reach out to the NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to explore opportunities
+to spotlight your work.
 
-Join us in building a vibrant, collaborative ecosystem where creativity and technology intersect. Your
-contributions can make a significant impact on the Isaac Lab community and beyond!
+You can also join the conversation on the [Omniverse Discord](https://discord.com/invite/nvidiaomniverse) to
+connect with other developers, share your projects, and help grow a vibrant, collaborative ecosystem
+where creativity and technology intersect. Your contributions can make a meaningful impact on the Isaac Lab community and beyond!
 
 ## License
 

diff --git a/docs/source/_static/tasks/locomotion/agility_digit_flat.jpg b/docs/source/_static/tasks/locomotion/agility_digit_flat.jpg
diff --git a/docs/source/_static/tasks/locomotion/agility_digit_loco_manip.jpg b/docs/source/_static/tasks/locomotion/agility_digit_loco_manip.jpg
diff --git a/docs/source/_static/tasks/locomotion/agility_digit_rough.jpg b/docs/source/_static/tasks/locomotion/agility_digit_rough.jpg
diff --git a/docs/source/how-to/add_own_library.rst b/docs/source/how-to/add_own_library.rst
@@ -63,7 +63,7 @@ Integrating a new library
 
 Adding a new library to Isaac Lab is similar to using a different version of a library. You can install the library
 in your Python environment and use it in your experiments. However, if you want to integrate the library with
-Isaac Lab, you can will first need to make a wrapper for the library, as explained in
+Isaac Lab, you will first need to make a wrapper for the library, as explained in
 :ref:`how-to-env-wrappers`.
 
 The following steps can be followed to integrate a new library with Isaac Lab:

diff --git a/docs/source/overview/core-concepts/actuators.rst b/docs/source/overview/core-concepts/actuators.rst
@@ -35,6 +35,7 @@ maximum effort:
 .. math::
 
     \tau_{j, computed} & = k_p * (q_{des} - q) + k_d * (\dot{q}_{des} - \dot{q}) + \tau_{ff} \\
+    \tau_{j, max} & = \gamma \times \tau_{motor, max} \\
     \tau_{j, applied} & = clip(\tau_{computed}, -\tau_{j, max}, \tau_{j, max})
 
 

diff --git a/docs/source/overview/environments.rst b/docs/source/overview/environments.rst
@@ -214,6 +214,12 @@ We provide environments for both disassembly and assembly.
       wget https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run
       sudo sh cuda_12.8.0_570.86.10_linux.run
 
+  When using conda, cuda toolkit can be installed with:
+
+  .. code-block:: bash
+
+      conda install cudatoolkit
+
   For addition instructions and Windows installation, please refer to the `CUDA installation page <https://developer.nvidia.com/cuda-12-8-0-download-archive>`_.
 
 * |disassembly-link|: The plug starts inserted in the socket. A low-level controller lifts the plug out and moves it to a random position. This process is purely scripted and does not involve any learned policy. Therefore, it does not require policy training or evaluation. The resulting trajectories serve as demonstrations for the reverse process, i.e., learning to assemble. To run disassembly for a specific task: ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py --assembly_id=ASSEMBLY_ID --disassembly_dir=DISASSEMBLY_DIR``. All generated trajectories are saved to a local directory ``DISASSEMBLY_DIR``.
@@ -288,6 +294,12 @@ Environments based on legged locomotion tasks.
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
     | |velocity-rough-g1|          | |velocity-rough-g1-link|                     | Track a velocity command on rough terrain with the Unitree G1 robot          |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
+    | |velocity-flat-digit|        | |velocity-flat-digit-link|                   | Track a velocity command on flat terrain with the Agility Digit robot        |
+    +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
+    | |velocity-rough-digit|       | |velocity-rough-digit-link|                  | Track a velocity command on rough terrain with the Agility Digit robot       |
+    +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
+    | |tracking-loco-manip-digit|  | |tracking-loco-manip-digit-link|             | Track a root velocity and hand pose command with the Agility Digit robot     |
+    +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
 
 .. |velocity-flat-anymal-b-link| replace:: `Isaac-Velocity-Flat-Anymal-B-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/flat_env_cfg.py>`__
 .. |velocity-rough-anymal-b-link| replace:: `Isaac-Velocity-Rough-Anymal-B-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/rough_env_cfg.py>`__
@@ -318,6 +330,9 @@ Environments based on legged locomotion tasks.
 .. |velocity-flat-g1-link| replace:: `Isaac-Velocity-Flat-G1-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/flat_env_cfg.py>`__
 .. |velocity-rough-g1-link| replace:: `Isaac-Velocity-Rough-G1-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py>`__
 
+.. |velocity-flat-digit-link| replace:: `Isaac-Velocity-Flat-Digit-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/flat_env_cfg.py>`__
+.. |velocity-rough-digit-link| replace:: `Isaac-Velocity-Rough-Digit-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/digit/rough_env_cfg.py>`__
+.. |tracking-loco-manip-digit-link| replace:: `Isaac-Tracking-Flat-Digit-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/tracking/config/digit/loco_manip_env_cfg.py>`__
 
 .. |velocity-flat-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_flat.jpg
 .. |velocity-rough-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_rough.jpg
@@ -336,6 +351,9 @@ Environments based on legged locomotion tasks.
 .. |velocity-rough-h1| image:: ../_static/tasks/locomotion/h1_rough.jpg
 .. |velocity-flat-g1| image:: ../_static/tasks/locomotion/g1_flat.jpg
 .. |velocity-rough-g1| image:: ../_static/tasks/locomotion/g1_rough.jpg
+.. |velocity-flat-digit| image:: ../_static/tasks/locomotion/agility_digit_flat.jpg
+.. |velocity-rough-digit| image:: ../_static/tasks/locomotion/agility_digit_rough.jpg
+.. |tracking-loco-manip-digit| image:: ../_static/tasks/locomotion/agility_digit_loco_manip.jpg
 
 Navigation
 ~~~~~~~~~~
@@ -554,6 +572,10 @@ Environments based on fixed-arm manipulation tasks.
 Comprehensive List of Environments
 ==================================
 
+For environments that have a different task name listed under ``Inference Task Name``, please use the Inference Task Name
+provided when running ``play.py`` or any inferencing workflows. These tasks provide more suitable configurations for
+inferencing, including reading from an already trained checkpoint and disabling runtime perturbations used for training.
+
 .. list-table::
     :widths: 33 25 19 25
 
@@ -761,6 +783,10 @@ Comprehensive List of Environments
       -
       - Manager Based
       -
+    * - Isaac-Tracking-LocoManip-Digit-v0
+      - Isaac-Tracking-LocoManip-Digit-Play-v0
+      - Manager Based
+      - **rsl_rl** (PPO)
     * - Isaac-Navigation-Flat-Anymal-C-v0
       - Isaac-Navigation-Flat-Anymal-C-Play-v0
       - Manager Based
@@ -869,6 +895,10 @@ Comprehensive List of Environments
       - Isaac-Velocity-Flat-Cassie-Play-v0
       - Manager Based
       - **rsl_rl** (PPO), **skrl** (PPO)
+    * - Isaac-Velocity-Flat-Digit-v0
+      - Isaac-Velocity-Flat-Digit-Play-v0
+      - Manager Based
+      - **rsl_rl** (PPO)
     * - Isaac-Velocity-Flat-G1-v0
       - Isaac-Velocity-Flat-G1-Play-v0
       - Manager Based
@@ -884,7 +914,7 @@ Comprehensive List of Environments
     * - Isaac-Velocity-Flat-Unitree-A1-v0
       - Isaac-Velocity-Flat-Unitree-A1-Play-v0
       - Manager Based
-      - **rsl_rl** (PPO), **skrl** (PPO)
+      - **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO)
     * - Isaac-Velocity-Flat-Unitree-Go1-v0
       - Isaac-Velocity-Flat-Unitree-Go1-Play-v0
       - Manager Based
@@ -913,6 +943,10 @@ Comprehensive List of Environments
       - Isaac-Velocity-Rough-Cassie-Play-v0
       - Manager Based
       - **rsl_rl** (PPO), **skrl** (PPO)
+    * - Isaac-Velocity-Rough-Digit-v0
+      - Isaac-Velocity-Rough-Digit-Play-v0
+      - Manager Based
+      - **rsl_rl** (PPO)
     * - Isaac-Velocity-Rough-G1-v0
       - Isaac-Velocity-Rough-G1-Play-v0
       - Manager Based
@@ -924,7 +958,7 @@ Comprehensive List of Environments
     * - Isaac-Velocity-Rough-Unitree-A1-v0
       - Isaac-Velocity-Rough-Unitree-A1-Play-v0
       - Manager Based
-      - **rsl_rl** (PPO), **skrl** (PPO)
+      - **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO)
     * - Isaac-Velocity-Rough-Unitree-Go1-v0
       - Isaac-Velocity-Rough-Unitree-Go1-Play-v0
       - Manager Based

diff --git a/docs/source/overview/reinforcement-learning/rl_existing_scripts.rst b/docs/source/overview/reinforcement-learning/rl_existing_scripts.rst
@@ -187,7 +187,7 @@ Stable-Baselines3
 
 -  Training an agent with
    `Stable-Baselines3 <https://stable-baselines3.readthedocs.io/en/master/index.html>`__
-   on ``Isaac-Cartpole-v0``:
+   on ``Isaac-Velocity-Flat-Unitree-A1-v0``:
 
    .. tab-set::
       :sync-group: os
@@ -200,14 +200,13 @@ Stable-Baselines3
             # install python module (for stable-baselines3)
             ./isaaclab.sh -i sb3
             # run script for training
-            # note: we set the device to cpu since SB3 doesn't optimize for GPU anyway
-            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --headless --device cpu
+            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless
             # run script for playing with 32 environments
-            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Cartpole-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
+            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
             # run script for playing a pre-trained checkpoint with 32 environments
-            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Cartpole-v0 --num_envs 32 --use_pretrained_checkpoint
+            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint
             # run script for recording video of a trained agent (requires installing `ffmpeg`)
-            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Cartpole-v0 --headless --video --video_length 200
+            ./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200
 
       .. tab-item:: :icon:`fa-brands fa-windows` Windows
          :sync: windows
@@ -217,14 +216,13 @@ Stable-Baselines3
             :: install python module (for stable-baselines3)
             isaaclab.bat -i sb3
             :: run script for training
-            :: note: we set the device to cpu since SB3 doesn't optimize for GPU anyway
-            isaaclab.bat -p scripts\reinforcement_learning\sb3\train.py --task Isaac-Cartpole-v0 --headless --device cpu
+            isaaclab.bat -p scripts\reinforcement_learning\sb3\train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless
             :: run script for playing with 32 environments
-            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Cartpole-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
+            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
             :: run script for playing a pre-trained checkpoint with 32 environments
-            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Cartpole-v0 --num_envs 32 --use_pretrained_checkpoint
+            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint
             :: run script for recording video of a trained agent (requires installing `ffmpeg`)
-            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Cartpole-v0 --headless --video --video_length 200
+            isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200
 
 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>``

diff --git a/docs/source/overview/reinforcement-learning/rl_frameworks.rst b/docs/source/overview/reinforcement-learning/rl_frameworks.rst
@@ -71,17 +71,18 @@ Training Performance
 --------------------
 
 We performed training with each RL library on the same ``Isaac-Humanoid-v0`` environment
-with ``--headless`` on a single RTX 4090 GPU
+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.
 
+
 +--------------------+-----------------+
 | RL Library         | Time in seconds |
 +====================+=================+
-| RL-Games           | 203             |
+| RL-Games           | 207             |
 +--------------------+-----------------+
-| SKRL               | 204             |
+| SKRL               | 208             |
 +--------------------+-----------------+
-| RSL RL             | 207             |
+| RSL RL             | 199             |
 +--------------------+-----------------+
-| Stable-Baselines3  | 6320            |
+| Stable-Baselines3  | 322             |
 +--------------------+-----------------+
diff --git a/docs/source/setup/installation/binaries_installation.rst b/docs/source/setup/installation/binaries_installation.rst
@@ -451,6 +451,10 @@ On Windows machines, please terminate the process from Command Prompt using
 
 If you see this, then the installation was successful! |:tada:|
 
+If you see an error ``ModuleNotFoundError: No module named 'isaacsim'``, ensure that the conda environment is activated
+and ``source _isaac_sim/setup_conda_env.sh`` has been executed.
+
+
 Train a robot!
 ~~~~~~~~~~~~~~~
 

diff --git a/docs/source/setup/walkthrough/api_env_design.rst b/docs/source/setup/walkthrough/api_env_design.rst
@@ -78,7 +78,7 @@ the prim path with ``/World/envs/env_.*/Robot`` we are implicitly saying that ev
 The Environment
 -----------------
 
-Next, let's take a look at the contents of the other python file in our task directory: ``isaac_lab_tutorial_env_cfg.py``
+Next, let's take a look at the contents of the other python file in our task directory: ``isaac_lab_tutorial_env.py``
 
 .. code-block:: python
 

diff --git a/docs/source/setup/walkthrough/training_jetbot_gt.rst b/docs/source/setup/walkthrough/training_jetbot_gt.rst
@@ -104,7 +104,7 @@ Next, we need to expand the initialization and setup steps to construct the data
 
 Most of this is setting up the book keeping for the commands and markers, but the command initialization and the yaw calculations are worth diving into. The commands
 are sampled from a multivariate normal distribution via ``torch.randn`` with the z component fixed to zero and then normalized to unit length. In order to point our
-command markers along these vectors, we need to rotate the base arrow mesh appropriately. This means we need to define a `quaternion <https://en.wikipedia.org/wiki/Quaternion>`_` that will rotate the arrow
+command markers along these vectors, we need to rotate the base arrow mesh appropriately. This means we need to define a `quaternion <https://en.wikipedia.org/wiki/Quaternion>`_ that will rotate the arrow
 prim about the z axis by some angle defined by the command. By convention, rotations about the z axis are called a "yaw" rotation (akin to roll and pitch).
 
 Luckily for us, Isaac Lab provides a utility to generate a quaternion from an axis of rotation and an angle: :func:`isaaclab.utils.math.quat_from_axis_angle`, so the only

diff --git a/docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst b/docs/source/setup/walkthrough/training_jetbot_reward_exploration.rst
@@ -75,6 +75,8 @@ from linear algebra! Replace the contents of ``_get_observations`` with the foll
         observations = {"policy": obs}
         return observations
 
+We also need to **edit the ``IsaacLabTutorialEnvCfg`` to set the observation space back to 3** which includes the dot product, the z component of the cross product, and the forward speed.
+
 The dot or inner product tells us how aligned two vectors are as a single scalar quantity.  If they are very aligned and pointed in the same direction, then the inner
 product will be large and positive, but if they are aligned and in opposite directions, it will be large and negative.  If two vectors are
 perpendicular, the inner product is zero. This means that the inner product between the forward vector and the command vector can tell us

diff --git a/scripts/reinforcement_learning/rl_games/play.py b/scripts/reinforcement_learning/rl_games/play.py
@@ -188,7 +188,7 @@ def main():
                         s[:, dones, :] = 0.0
         if args_cli.video:
             timestep += 1
-            # Exit the play loop after recording one video
+            # exit the play loop after recording one video
             if timestep == args_cli.video_length:
                 break
 

diff --git a/scripts/reinforcement_learning/rl_games/train.py b/scripts/reinforcement_learning/rl_games/train.py
@@ -194,10 +194,11 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
             entity=args_cli.wandb_entity,
             name=experiment_name,
             sync_tensorboard=True,
-            config=agent_cfg,
             monitor_gym=True,
             save_code=True,
         )
+        wandb.config.update({"env_cfg": env_cfg.to_dict()})
+        wandb.config.update({"agent_cfg": agent_cfg})
 
     if args_cli.checkpoint is not None:
         runner.run({"train": True, "play": False, "sigma": train_sigma, "checkpoint": resume_path})