Quickstart
This page takes you from a fresh checkout to training, replaying, and inspecting
your first task. For prerequisites, see System requirements.
Run all commands from the Isaac Lab repository root with uv run. uv
creates and manages the project environment for you.
Install and run your first task
Install uv, clone Isaac Lab, and enter the repository:
Train Cartpole with the Newton MJWarp physics backend (its default, selected explicitly here to show the syntax) and open the Newton visualizer:
Training outputs, including checkpoints, are saved under logs/. Add
--help to any command to see its available arguments:
Configure an editor (optional)
To enable import completion and debugging in VS Code or Cursor, generate the machine-local editor configuration from the repository root:
If you use the isaacsim extra, include it so the command can discover the
Isaac Sim extensions:
VS Code uses Pylance. Cursor users should install basedpyright instead. See Editor setup for complete editor and troubleshooting instructions.
The same train and play workflow applies across classic control, locomotion, and manipulation tasks.
uv run installs the core dependencies automatically. To use an optional
integration, add --extra <name> before isaaclab. You can enable multiple
extras with a comma-separated list. For example:
Extras make optional capabilities available; task selectors choose which capabilities the task uses
For example, --extra ovphysx makes the OV PhysX integration available, while
physics=ovphysx selects it for the task. You can combine extras as needed. The --extra all
shortcut installs the curated ov, rl-games, sb3, skrl, rsl-rl, rerun,
and viser extras. Isaac Sim, standalone importers, and specialized extras such as skrl-jax,
rlinf, mimic, teleop, tetrahedralization, video, leapp, and torchrl
are not included; add them explicitly. See
Optional extras for the complete list.
Choose an RL library
Pass --rl_library to train or play to choose the RL framework. If
you omit it, Isaac Lab uses the default registered for the task. Most core tasks
default to rsl_rl, which is included in the standard uv run environment
and is a good starting point for GPU-based training.
RL libraries differ in their supported algorithms, tasks, and workflows. See Choose an RL library for a detailed comparison.
The five commands to know
All task commands accept --task <task_name>. Start by listing the registered tasks. The list shows
which RL libraries and physics= / renderer= / presets= selectors each task supports, and
runs without Isaac Sim:
All supported RL libraries use --checkpoint to choose a checkpoint for
playback. See /source/concepts/reinforcement_learning for the
complete training and playback reference.
Choose a backend
Isaac Lab supports multiple physics and rendering backends. Use
physics=<backend> to choose the physics implementation. For camera tasks,
use renderer=<backend> to choose the renderer.
Available backends depend on the task configuration. Use the task’s help output
to see its supported selectors:
Use presets=<name> to apply a task-specific configuration preset. For
example:
Many tasks default to Isaac Sim PhysX, which needs Isaac Sim, while others, such as Cartpole,
already default to Newton. Pick the setup path that matches your install and set its physics=
selector whenever a task’s default backend doesn’t match it. Add renderer= only for tasks
with cameras or renderer presets, such as Isaac-Cartpole-Camera; other tasks reject it. Run
uv run isaaclab list_envs --show_presets to see which combinations a task can launch:
See /source/concepts/backends_and_presets for backend and preset selection, and /source/features/hydra for arbitrary configuration overrides.
Visualize a task
Use --viz (or --visualizer) to choose one or more visualizers during
training or playback. To use multiple visualizers, pass a comma-separated list
without spaces, such as --viz newton_gl,rerun.
For example, open the same task in both Newton and Rerun:
See https://isaac-sim.github.io/IsaacLab/release/3.0.0/source/concepts/visualization.html for visualizer setup and configuration.
Play a trained policy
First, train Cartpole to create a checkpoint:
Then play the latest checkpoint in the Newton visualizer:
Choose a checkpoint with one of the following options:
Benchmark a task
isaaclab benchmark takes a workflow name as its first argument. Start with
runtime to measure environment-step capacity without a policy:
See Benchmarking for benchmark fundamentals and Multi-GPU and Multi-Node Training for multi-GPU training and benchmarks.
Next steps
- Browse registered environments and build a command: https://isaac-sim.github.io/IsaacLab/release/3.0.0/source/setup/environments.html
- Learn how backends and presets fit together: /source/concepts/backends_and_presets
- Learn how to override task configuration: /source/features/hydra
- Find tutorials and task-focused documentation: /source/how-to/index
- Read the installation options and troubleshooting guide: Installation