Warning
Alpha Software — Not an Early Access or General Availability Release.
Isaac Lab-Arena v0.2.x is an early code release intended to give the community a practical starting point to experiment, provide feedback, and influence future design direction. APIs are unstable and will change. Features are incomplete. Documentation is evolving. Do not use this in production. See Project Status for details.
Note
Changes on main contains an in development version based on v0.2.0, based on Isaac Lab 3.0.
Isaac Lab-Arena is an open-source extension to NVIDIA Isaac Lab for simplified task curation and robotic policy evaluation at scale. It provides a composable architecture where environments are assembled on-the-fly from independent, reusable building blocks — eliminating the redundant boilerplate that plagues traditional task library development.
Instead of hand-writing and maintaining a separate configuration for every combination of robot, object, and scenario, Arena lets you compose environments from three independent primitives:
| Primitive | Description |
|---|---|
| Scene | The physical environment layout — a collection of objects, furniture, fixtures |
| Embodiment | The robot and its observations, actions, sensors, and controllers |
| Task | The objective — what the robot should accomplish (pick-and-place, open door, etc.) |
The ArenaEnvBuilder composes these primitives into a standard ManagerBasedRLEnvCfg that runs natively in Isaac Lab.
With the rise of generalist robot policies (e.g., GR00T N, pi0, SmolVLA), there is an urgent need to evaluate these policies across many diverse tasks and environments. Traditional approaches suffer from:
- Code duplication — each task variation (different object, different robot) requires a near-copy of the same configuration
- Maintenance burden — N robots × M objects × K scenes = an explosion of configs to keep in sync
- Slow iteration — researchers spend more time wrangling configs than running experiments
Arena solves this by making environment variation a first-class concept. Swap an object, change a robot, or modify a scene — all without duplicating a single line of task logic.
- LEGO-like Composable Environments — Mix and match scenes, embodiments, and tasks independently
- On-the-fly Assembly — Environments are built at runtime; no duplicate config files to maintain.
- New Sequential Task Chaining — Chain atomic skills (e.g. Pick + Walk + Place + …) to create complex long-horizon tasks.
- New Natural Language Object Placement — Define scene layouts using semantic relationships like "on" or "next to", instead of manually specified coordinates.
- Integrated Evaluation — Extensible metrics and evaluation pipelines for policy benchmarking
- New Large-scale Parallel Evaluations with Heterogeneous Objects — Evaluate policy on multiple parallel environments, each with different objects, to maximize evaluation throughput.
- New RL Workflow Support and Seamless Interoperation with Isaac Lab: Plug Isaac Lab - Arena environments into Isaac Lab workflows for Reinforcement learning and Data generation for imitation learning.
- Linux (Ubuntu 22.04+)
- NVIDIA GPU (see Isaac Sim hardware requirements)
- Docker and NVIDIA Container Toolkit
- Git
Isaac Lab-Arena currently supports installation from source inside a Docker container.
# 1. Clone the repository
git clone git@github.com:isaac-sim/IsaacLab-Arena.git
cd IsaacLab-Arena
git submodule update --init --recursive
# 2. Launch the Docker container
# Base container (recommended for development):
./docker/run_docker.sh
# Or with GR00T dependencies (for policy training/evaluation):
./docker/run_docker.sh -g
# 3. Verify the installation
/isaac-sim/python.sh -m pytest -sv -m "not with_cameras" isaaclab_arena/tests/Note: The Docker script automatically mounts
$HOME/datasets,$HOME/models, and$HOME/evalfrom your host into the container.
For detailed setup instructions (including server-client mode for GR00T), see the Installation Guide.
Compose a Franka arm in a kitchen scene with a couple of objects:
from isaaclab_arena.assets.asset_registry import AssetRegistry
from isaaclab_arena.cli.isaaclab_arena_cli import get_isaaclab_arena_cli_parser
from isaaclab_arena.environments.arena_env_builder import ArenaEnvBuilder
from isaaclab_arena.environments.isaaclab_arena_environment import IsaacLabArenaEnvironment
from isaaclab_arena.scene.scene import Scene
asset_registry = AssetRegistry()
# Select building blocks
background = asset_registry.get_asset_by_name("kitchen")()
embodiment = asset_registry.get_asset_by_name("franka_ik")()
cracker_box = asset_registry.get_asset_by_name("cracker_box")()
tomato_soup_can = asset_registry.get_asset_by_name("tomato_soup_can")()
# Compose the environment
scene = Scene(assets=[background, cracker_box, tomato_soup_can])
env_cfg = IsaacLabArenaEnvironment(
name="franka_kitchen_example",
embodiment=embodiment,
scene=scene,
)
args_cli = get_isaaclab_arena_cli_parser().parse_args([])
env_builder = ArenaEnvBuilder(env_cfg, args_cli)
env = env_builder.make_registered()
env.reset()Explore more examples in the documentation, including:
| Example | Description |
|---|---|
| Imitation Learning | |
| G1 Loco-Manipulation Pick & Place | G1 humanoid navigates, picks up a box, and places it in a bin |
| GR1 Open Microwave Door | GR1 upper-body manipulation of an articulated microwave door |
| GR1 Sequential Pick & Place and Close Door | GR1 picks an object, places it in a fridge, and closes the door |
| Reinforcement Learning | |
| Franka Lift Object | Franka Panda grasps and lifts objects to target positions (PhysX) |
| Dexsuite Kuka Allegro Lift (Newton) | Dexterous object lifting with Kuka Allegro hand (Newton physics, experimental) |
IsaacLab-Arena/
├── isaaclab_arena/ # Core framework (environments, tasks, scenes, embodiments)
├── isaaclab_arena_environments/ # Concrete environment definitions
├── isaaclab_arena_examples/ # Policy and relation examples
├── isaaclab_arena_g1/ # Unitree G1 humanoid embodiment + examples
├── isaaclab_arena_gr00t/ # GR00T policy integration
├── docker/ # Docker configurations and launch scripts
├── docs/ # Sphinx documentation source
├── osmo/ # Cloud deployment configs (OSMO)
├── submodules/ # Git submodules (Isaac Lab, etc.)
├── setup.py # Package installation
├── CONTRIBUTING.md # Contribution guidelines
└── LICENSE.md # Apache 2.0 license
| Isaac Lab-Arena | Isaac Lab | Isaac Sim | Python |
|---|---|---|---|
main |
3.0.0 | 6.0.0 | ≥ 3.12 |
release/0.2.0 |
3.0.0 | 6.0.0 | ≥ 3.12 |
feature/arena_v0.2_on_lab_2.3 |
2.3.0 | 5.1.0 | ≥ 3.10 |
release/0.1.1 |
2.3.0 | 5.0.0 | ≥ 3.10 |
Isaac Lab-Arena is in alpha (v0.2.x). This is important to understand:
| What This Means | Details |
|---|---|
| Not EA / GA | This is not an Early Access or General Availability release. It is a very early community code drop. |
| APIs will break | Public interfaces are under active development and will change without deprecation warnings. |
| Features are incomplete | Core capabilities like agentic task generation, non-sequential long horizon tasks, easy-to-configure sensitivity analysis, enhanced heterogeneity across parallel evaluations and pip install support are planned but not yet implemented. |
| Docker-only install | Source installation in a Docker container is the only supported method. |
| Limited testing | The main branch contains the latest code but may not be fully tested. Use release/0.2.0 for the most stable experience. |
Isaac Lab-Arena is part of a growing ecosystem of tools and benchmarks. NVIDIA and partners are building industrial and academic benchmarks on the unified Isaac Lab-Arena core, so you can reuse building blocks (tasks, scenes, metrics, and datasets) for your custom evaluations.
- Lightwheel RoboFinals — High-fidelity industrial benchmarks.
- Lightwheel RoboCasa Tasks — 138+ open-source tasks, 50 datasets per task, 7+ robots.
- Lightwheel LIBERO Tasks — Adapted LIBERO benchmarks.
- RoboTwin 2.0 — Extended simulation benchmarks using Arena; Arxiv.
- LeRobot Environment Hub — Share and discover Arena environments on Hugging Face.
NIST Board 1, NVIDIA Isaac GR00T Industrial Benchmarks, NVIDIA DexBench, NVIDIA RoboLab, and more benchmarks are coming soon.
We encourage the community to build and publish benchmarks on Isaac Lab-Arena. The recommended workflow:
- Maintain your benchmark in your own repository. Create a branch or package that integrates with Isaac Lab-Arena (e.g. an
IsaacLab-Arenabranch). See RoboTwin for a reference example. For detailed setup instructions — including repository layout, Dockerfile setup, and how to register custom environments/robots/tasks — see the Arena in Your Repository guide. - Reference your benchmark and Isaac Lab-Arena in publications. When publishing on ArXiv or elsewhere, cite both your benchmark (by name, with a link to your repository) and Isaac Lab-Arena as the underlying evaluation framework.
- List it here. Open a PR to add your benchmark to the Published Benchmarks list above. This README serves as the single source of truth for the Arena benchmark ecosystem so that community can discover and reuse.
We welcome contributions — bug reports, feature suggestions, and code. This is a pre-alpha project, so community input directly shapes the framework's direction.
- Read the Contribution Guidelines
- Sign off your commits (DCO required — see
CONTRIBUTING.md) - Open a Pull Request
Areas where contributions are especially valuable:
- New task definitions and benchmark suites
- Additional robot embodiments and scene assets
- Sim-to-real validated evaluation methods
- Documentation improvements and tutorials
- Questions & Ideas — GitHub Discussions
- Bug Reports — GitHub Issues
- Isaac Sim Questions — NVIDIA Forums
- Community Chat — Omniverse Discord
Isaac Lab-Arena is released under the Apache 2.0 License.
Note that Isaac Lab-Arena requires Isaac Sim, which includes components under proprietary licensing terms. See the Isaac Sim license for details.
If you use Isaac Lab-Arena in your research, please cite:
@misc{isaaclab-arena2025,
title = {Isaac Lab-Arena: Composable Environment Creation and Policy Evaluation for Robotics},
author = {{NVIDIA Isaac Lab-Arena Contributors}},
year = {2025},
url = {https://github.com/isaac-sim/IsaacLab-Arena}
}If you use Isaac Lab (the underlying framework), please also cite the Isaac Lab paper.
Isaac Lab-Arena builds on NVIDIA Isaac Lab, with the evaluation and task layers designed in close collaboration with Lightwheel. We thank the Isaac Lab team and the broader robotics community for their foundational work.
Isaac Lab-Arena · Alpha · Documentation · GitHub
Made with ❤️ by the NVIDIA Robotics Team