isaac-sim · peterd-NV · Sep 4, 2026 · Sep 4, 2026
@@ -1,7 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-name: Autodata CI
+name: AutoData CI
 
 on:
   pull_request:

@@ -1,7 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-name: Autodata Nightly
+name: AutoData Nightly
 
 # 03:30 UTC is off-peak and offset from other repos' nightlies to reduce GPU-runner contention.
 on:

@@ -1,6 +1,6 @@
-## Autodata Contribution Rules
+## AutoData Contribution Rules
 
-This document describes the rules for contributing to Autodata
+This document describes the rules for contributing to AutoData
 
 
 #### Signing Your Work

@@ -1,6 +1,6 @@
-# Autodata Developers and Contributors
+# AutoData Developers and Contributors
 
-This is the official list of Autodata project developers and contributors.
+This is the official list of AutoData project developers and contributors.
 
 Guidelines for modifications:
 

@@ -1,6 +1,6 @@
 <div align="center">
 
-# Autodata
+# AutoData
 
 ### Scalable Robot Demonstration Generation for Robot Learning
 
@@ -20,27 +20,27 @@
 
 ## Overview
 
-**Autodata** is a trajectory data-generation framework built on
+**AutoData** is a trajectory data-generation framework built on
 [NVIDIA Isaac Lab](https://github.com/isaac-sim/IsaacLab) and
 [Isaac Lab-Arena](https://github.com/isaac-sim/IsaacLab-Arena). Given a small set of annotated human
 demonstrations, it uses parallel simulation environments to generate diverse datasets of successful robot
 demonstrations for robot learning.
 
-Autodata splits demonstrations into object-relative skill segments. During generation, it transforms those
+AutoData splits demonstrations into object-relative skill segments. During generation, it transforms those
 segments to new scene configurations, connects them into complete trajectories, executes them in simulation, and
 records successful demonstrations in HDF5 datasets.
 
 <p align="center">
-  <img src="docs/images/autodata.gif" alt="Autodata generating robot demonstrations in parallel" width="100%">
+  <img src="docs/images/autodata.gif" alt="AutoData generating robot demonstrations in parallel" width="100%">
 </p>
 
-## Why Autodata?
+## Why AutoData?
 
 Robot-learning policies require large and diverse collections of successful demonstrations. Gathering all of
 that data through human teleoperation is slow and expensive, even though a small set of demonstrations often already
 contains the task's essential skills.
 
-Autodata scales those demonstrations across randomized object placements and scene configurations, reducing
+AutoData scales those demonstrations across randomized object placements and scene configurations, reducing
 the amount of manual collection needed to produce datasets for policy training.
 
 ## Key Features
@@ -162,25 +162,25 @@ For test-suite details and common commands, see
 
 ## License
 
-Autodata is released under the [Apache License 2.0](LICENSE.md).
+AutoData is released under the [Apache License 2.0](LICENSE.md).
 
 Third-party open-source software notices are provided in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).
 
 Additional component-specific licenses are stored in [`docs/licenses/`](docs/licenses/). NVIDIA cuRobo is governed
 by the [NVIDIA Isaac Lab Additional Software and Materials License](docs/licenses/curobo-license.txt).
 
-Autodata depends on Isaac Sim, which includes components distributed under proprietary licensing terms. See
+AutoData depends on Isaac Sim, which includes components distributed under proprietary licensing terms. See
 the [Isaac Sim license](https://docs.isaacsim.omniverse.nvidia.com/latest/common/NVIDIA_Omniverse_License_Agreement.html)
 for details.
 
 ## Citation
 
-If you use Autodata in your research, please cite:
+If you use AutoData in your research, please cite:
 
 ```bibtex
 @misc{autodata2026,
-    title  = {Autodata: Scalable Robot Demonstration Generation for Robot Learning},
-    author = {{NVIDIA Autodata Contributors}},
+    title  = {AutoData: Scalable Robot Demonstration Generation for Robot Learning},
+    author = {{NVIDIA AutoData Contributors}},
     year   = {2026},
     url    = {https://github.com/isaac-sim/AutoData}
 }
@@ -193,7 +193,7 @@ Depending on the generation algorithm used, please also cite the original
 
 ## Acknowledgements
 
-Autodata builds on NVIDIA Isaac Sim, Isaac Lab, and Isaac Lab-Arena. Its data-generation workflows incorporate
+AutoData builds on NVIDIA Isaac Sim, Isaac Lab, and Isaac Lab-Arena. Its data-generation workflows incorporate
 ideas from MimicGen, DexMimicGen, and SkillMimicGen, with cuRobo providing GPU-accelerated motion planning for
 SkillGen workflows.
 
@@ -204,6 +204,6 @@ foundational work.
 
 <div align="center">
 
-**Autodata** · [Documentation](https://isaac-sim.github.io/AutoData/) · [GitHub](https://github.com/isaac-sim/AutoData)
+**AutoData** · [Documentation](https://isaac-sim.github.io/AutoData/) · [GitHub](https://github.com/isaac-sim/AutoData)
 
 </div>
@@ -5,7 +5,7 @@ SPDX-License-Identifier: Apache-2.0
 
 # Third-Party Software Notices
 
-Autodata includes or directly depends on the following open-source software. Each entry reproduces the
+AutoData includes or directly depends on the following open-source software. Each entry reproduces the
 component's copyright and license notice and links to the canonical license text.
 
 ## IsaacLab-Arena

@@ -1,7 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Core data generation runtime for Autodata.
+"""Core data generation runtime for AutoData.
 
 Public entrypoints:
 

@@ -1,7 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Shared assertion helpers for the Autodata test suite."""
+"""Shared assertion helpers for the AutoData test suite."""
 
 import h5py
 import json

@@ -17,7 +17,7 @@ usage() {
 Usage: $(basename "$0") [-c] [-i] [-h]
 
   -c    Create conda env "${ENV_NAME}" with Python ${PYTHON_VERSION}
-  -i    Install Isaac Sim, PyTorch, Isaac Lab, Arena, and Autodata
+  -i    Install Isaac Sim, PyTorch, Isaac Lab, Arena, and AutoData
         into the "${ENV_NAME}" env (env must already exist; run with -c first)
   -h    Show this help
 
@@ -110,7 +110,7 @@ install_all() {
     echo ">>> Installing Isaac Lab - Arena"
     uv pip install --editable "${arena_root}"
 
-    echo ">>> Installing Autodata"
+    echo ">>> Installing AutoData"
     uv pip install --editable "${REPO_ROOT}"
 
     echo ">>> Installation complete."

@@ -40,7 +40,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 #######################################
 # Copy the Arena submodule once as it carries the nested IsaacLab checkout.
 # The .dockerignore drops Arena's other nested submodules (e.g. Isaac-GR00T)
-# which Autodata does not use.
+# which AutoData does not use.
 COPY submodules/IsaacLab-Arena ${WORKDIR}/submodules/IsaacLab-Arena
 
 # --- IsaacLab ---
@@ -60,7 +60,7 @@ RUN /isaac-sim/python.sh -m pip install --force-reinstall daqp==0.8.5
 RUN /isaac-sim/python.sh -m pip install -e ${WORKDIR}/submodules/IsaacLab-Arena
 
 ##################
-# Autodata
+# AutoData
 ##################
 # Copy the package sources + packaging metadata (exclude submodules installed above).
 COPY setup.py pyproject.toml README.md sitecustomize.py ${WORKDIR}/
@@ -96,7 +96,7 @@ RUN echo 'export PATH="/isaac-sim/kit/python/bin:${CUDA_HOME}/bin:${PATH}"' >> /
  && echo "alias pytest='/isaac-sim/python.sh -m pytest'" >> /etc/bash.bashrc \
  && echo "alias pre-commit='/isaac-sim/python.sh -m pre_commit'" >> /etc/bash.bashrc \
  && echo "alias ll='ls -alF --color=auto'" >> /etc/bash.bashrc \
- && echo "PS1='[Autodata] \[\e[0;32m\]\u \[\e[0;34m\]\w\[\e[0m\] \$ '" >> /etc/bash.bashrc \
+ && echo "PS1='[AutoData] \[\e[0;32m\]\u \[\e[0;34m\]\w\[\e[0m\] \$ '" >> /etc/bash.bashrc \
  && cp /etc/bash.bashrc /root/.bashrc
 
 # Set the entrypoint

@@ -56,7 +56,7 @@ while getopts ":d:crRvh" OPTION; do
         v) set -x ;;
         h)
             script_name=$(basename "$0")
-            echo "Build and run the Autodata dev container."
+            echo "Build and run the AutoData dev container."
             echo ""
             echo "Usage: $script_name [options] [command...]"
             echo ""
@@ -149,7 +149,7 @@ DOCKER_RUN_ARGS=(
     "--env" "ACCEPT_EULA=Y"
     "--env" "PRIVACY_CONSENT=Y"
     "--env" "ISAACLAB_PATH=${ISAACLAB_PATH}"
-    # Make Autodata's sitecustomize compatibility hook visible to direct upstream script entrypoints.
+    # Make AutoData's sitecustomize compatibility hook visible to direct upstream script entrypoints.
     "--env" "PYTHONPATH=${WORKDIR}"
     # Used by the entrypoint to recreate the host user inside the container.
     "--env" "DOCKER_RUN_USER_ID=$(id -u)"

@@ -1,4 +1,4 @@
-# Autodata Documentation — Developer Guide
+# AutoData Documentation — Developer Guide
 
 The docs are built on the **host machine** (not inside Docker) using a dedicated Python 3.12 venv.
 

@@ -1,7 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Small source-read macros for the Autodata docs.
+"""Small source-read macros for the AutoData docs.
 
 Mirrors the Arena docs tooling (``isaaclab_arena_doc_tools``): text substitutions applied to every
 RST page before parsing, so recurring snippets (docker commands, code links, clone URLs) are defined

@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html>
   <head>
-    <title>Redirecting to the latest Autodata documentation</title>
+    <title>Redirecting to the latest AutoData documentation</title>
     <meta charset="utf-8">
     <meta http-equiv="refresh" content="0; url=./main/index.html">
   </head>

@@ -17,7 +17,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = "Autodata"
+project = "AutoData"
 copyright = "2026, NVIDIA"
 author = "NVIDIA"
 released = False  # Indicates if this is a public or internal version of the repo.
@@ -70,7 +70,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 html_theme = "nvidia_sphinx_theme"
-html_title = f"Autodata {AUTODATA_VERSION_NUMBER}"
+html_title = f"AutoData {AUTODATA_VERSION_NUMBER}"
 html_show_sphinx = False
 html_theme_options = {
     "copyright_override": {"start": 2026},

@@ -1,7 +1,7 @@
-Welcome to Autodata!
+Welcome to AutoData!
 ====================
 
-``Autodata`` is a trajectory data-generation framework built on top of
+``AutoData`` is a trajectory data-generation framework built on top of
 `Isaac Lab <https://isaac-sim.github.io/IsaacLab/main/index.html>`_ and
 `Isaac Lab-Arena <https://github.com/isaac-sim/IsaacLab-Arena>`_.
 Given a handful of annotated human demonstrations, it uses parallel simulation environments to
@@ -11,9 +11,9 @@ demonstration segments.
 .. figure:: images/autodata.gif
    :width: 100%
    :align: center
-   :alt: Autodata
+   :alt: AutoData
 
-   Autodata
+   AutoData
 
 
 The Problem
@@ -24,14 +24,14 @@ demonstrations, and collecting those by human teleoperation is slow and expensiv
 what a policy needs to learn from a thousand demonstrations is already contained in ten: the
 same skill repeated under different object placements.
 
-Autodata exploits that redundancy. A human demonstration is split into **subtasks** (each
+AutoData exploits that redundancy. A human demonstration is split into **subtasks** (each
 a contiguous segment in which the robot's end-effector motion is driven by a single reference
 object). Because each segment is object-relative, it can be *transformed* to a new scene
 configuration and replayed. Stitching transformed segments together turns a
 handful of demonstrations into an arbitrarily large dataset.
 
 
-Autodata
+AutoData
 ========
 
 Four pieces cooperate to generate data:
@@ -53,9 +53,9 @@ Four pieces cooperate to generate data:
    :width: 100%
    :align: center
    :target: _images/System_Architecture.svg
-   :alt: Autodata system architecture — contracts, typed data flow, modular generation and execution
+   :alt: AutoData system architecture — contracts, typed data flow, modular generation and execution
 
-   The Autodata architecture: declarative contracts feed the Datastream read interface,
+   The AutoData architecture: declarative contracts feed the Datastream read interface,
    which the data generator and its algorithm plug-ins consume to produce waypoints, actions,
    and finally recorded HDF5 episodes.
 
@@ -89,7 +89,7 @@ first generation with :doc:`pages/quickstart/first_data_generation`.
 License
 =======
 
-Autodata is licensed under the `Apache License 2.0
+AutoData is licensed under the `Apache License 2.0
 <https://github.com/isaac-sim/AutoData/blob/main/LICENSE.md>`_.
 
 

@@ -1,7 +1,7 @@
 Generation Algorithms
 =====================
 
-Autodata ships three generation algorithms, selected with ``--alg`` on the CLI (the
+AutoData ships three generation algorithms, selected with ``--alg`` on the CLI (the
 task descriptor's ``algo`` field independently selects which per-subtask ``algo_params``
 schema the descriptor is parsed with — keep the two consistent). All three plug into the
 :doc:`data generator <data_generator>` and share the same skeleton — split

@@ -1,7 +1,7 @@
 Architecture Overview
 =====================
 
-Autodata is organized around one idea: **the generation machinery should not know the
+AutoData is organized around one idea: **the generation machinery should not know the
 simulator or the robot directly.** Everything the generator needs is reachable through small,
 explicit interfaces — a task-descriptor YAML for the task, an embodiment YAML for the robot,
 and a read interface (the Datastream) for the live environment. New tasks, robots, and

@@ -1,7 +1,7 @@
 Task Descriptors
 ================
 
-A **task descriptor** is a YAML file that declares everything Autodata needs to know
+A **task descriptor** is a YAML file that declares everything AutoData needs to know
 about a task: its subtasks per end-effector, the signals that mark subtask boundaries,
 cross-arm constraints, and the data generation policy for the task. It is the single source of truth shared by
 the annotation tool and the data generator. Both load it, so annotations and generation can
@@ -74,7 +74,7 @@ Subtasks
 A **subtask** is a contiguous segment of a demonstration in which an end-effector's motion is
 dictated by a single object, such as reaching for a cube or placing it on another cube. A subtask
 ends and the next one begins when the object dictating the motion changes. During
-generation, Autodata selects a recorded subtask segment, transforms it into the current scene, connects the
+generation, AutoData selects a recorded subtask segment, transforms it into the current scene, connects the
 robot's current trajectory to the segment, and replays it.
 
 The ``subtasks`` section of the YAML schema groups subtasks by ``<eef_name>``. The eef name key must exactly match