Integrating GR00T with Isaac Lab: Custom Task Creation Beyond IsaacLabEval

[annahsu041]

Setup

• Isaac Sim: 5.0.0
• OS: Ubuntu 20.04
• GPU: RTX 4090 (Driver 570.133.07, CUDA 12.8)

First of all, thank you for your work. I am working on integrating GR00T-N1.5-3B with Isaac Lab (Franka Panda + RGB-D camera). My goal is to move beyond the default IsaacLabEval usage and gain finer control over the pipeline.

Problem

• I have already created tasks following the required file format, but none of them run successfully.
• I also attempted to load some custom modules to extend functionality, but the integration still fails.
• I would like to replicate the structure and workflow of to build my own tasks.

Questions

1. Are there examples or documentation showing how to create tasks outside of IsaacLabEval?
2. When mimicking , which core modules or classes must be inherited or overridden?
3. What is the recommended way to validate that my task definitions are correct (e.g., debugging tools or validation steps)?
4. Any best practices for integrating custom modules into the task pipeline without breaking compatibility?

[RandomOakForest]

Thank you for posting this. You can create and run GR00T-style tasks entirely outside IsaacLabEval by using Isaac Lab’s own project/task template, environment API, and managers pipeline. Below are concrete entry points and patterns to follow.¹² I'll move this post to our Discussions section for follow up, here is a summary, still under review, that may be of help for now.

1. Examples and docs for tasks outside IsaacLabEval

Use the built‑in template generator to create an external project or internal task; this gives you a clean, supported structure that is independent of IsaacLabEvalTasks.²

Key references:

• Template generator (external project + tasks): how to run ./isaaclab.sh --new, choose “external project”, and generate a custom task with RL/manager/direct workflows.²
• Tutorials index: contains full examples of custom environments, including camera‑based tasks and RL/IL workflows.³
• Available environments: shows how official tasks are registered and structured, useful to mirror Franka + camera setups.⁴

These three together give you a canonical structure for defining your own tasks and verifying that they register correctly without involving IsaacLabEval.

2. Core modules/classes to mirror when “mimicking” IsaacLabEvalTasks

IsaacLabEvalTasks is primarily a thin layer on top of Isaac Lab’s standard environment + managers abstraction. To reproduce the same pattern in your own project, focus on:⁵¹

• Environment base classes
  • Inherit from Isaac Lab’s task/env base, typically a subclass of the core environment (see tasks listed in “Available Environments” and the API index for omni.isaac.lab.envs).⁶⁴
  • Implement or override:
    • __init__ / configuration wiring (passing your EnvCfg / task config into the base class)
    • reset() / reset_idx() for environment initialization and episodic logic
    • step(actions) or pre_physics_step / post_physics_step if your workflow uses the manager patterns
• Configuration classes (“Cfg”)
  • Use @configclass style configs (e.g., Franka asset cfg, CameraCfg, SceneEntityCfg, observation and reward managers) as in official tasks.⁷⁴
  • Define:
    • Robot: Franka asset cfg (from existing Franka manipulation tasks)
    • Sensors: RGB‑D camera as a CameraCfg attached to the end‑effector or scene frame
    • Observations: image tensors + proprioceptive state fields that GR00T expects (joint positions, gripper state, possibly base pose)
    • Actions: action space matching your control backend (e.g., DifferentialIKControllerCfg or joint position controller)
• Managers and controllers
  • Use the standard controllers (e.g., differential IK) and managers for actions / observations so your task remains compatible with the rest of Isaac Lab tooling.¹⁶
  • For GR00T, you typically:
    • Map GR00T output to an intermediate action representation (e.g., end‑effector delta pose or joint deltas)
    • Feed this into an Isaac Lab controller (e.g., Differential IK) configured in your env config class

By starting from an auto‑generated task via ./isaaclab.sh --new and then examining official manipulation tasks in the “Available Environments” page, you can see exactly which base classes are used for Franka‑like setups and keep your own environment composition aligned.⁴²

3. Validating that a task is defined correctly

Before plugging in GR00T, treat your task as a standard Isaac Lab environment and validate it in isolation.³²

Recommended steps:

• Check registration and listing
  • Confirm your task is discoverable via python scripts/list_envs.py (or the equivalent script in your generated project).²
  • If it appears in the list, registration and entry‑point naming are correct.
• Run with zero/random agents
  • Use the generated helper scripts (or Isaac Lab’s zero_agent.py / random_agent.py) to run the environment and ensure:
    • No crashes in reset() or step()
    • Observations and actions have the expected shapes and dtypes
  • Example pattern from the template docs: python scripts/zero_agent.py --task=<Your-Task-Name> or similar command in the generated project.²
• Use tutorials as sanity checks
  • Start from a working tutorial task (e.g., a simple manipulation / Franka example), make small changes (add your camera, change reward) and confirm it still runs.⁷³
  • This incremental approach isolates whether the issue lies in:
    • Task registration
    • Scene or asset configuration
    • Observation/action definitions
• Unit tests for shapes and configs
  • It is often helpful to write a small script that:
    • Instantiates the env
    • Calls reset(), then one step() with a dummy action
    • Prints observation keys and tensor shapes to confirm compatibility with your GR00T preprocessing code

4. Best practices for integrating custom modules without breaking compatibility

To integrate custom GR00T‑related modules (policy wrappers, preprocessing, action mapping) and keep your Isaac Lab task stable:⁸⁵¹

• Isolate GR00T code from env code
  • Keep your Isaac Lab environment as a pure simulator that:
    • Exposes observations in a well‑defined dict
    • Accepts actions in a well‑defined tensor format
  • Wrap GR00T logic in a separate Python module/class (e.g., Gr00tPolicy), which:
    • Takes Isaac Lab observations (images + proprio)
    • Produces actions in the env’s expected space
• Use stable observation and action contracts
  • Define a clear contract:
    • Observation dict → GR00T input preprocessor
    • GR00T output → action adapter → env action tensor
  • If you need to change observation keys or shapes, do it at the adapter layer rather than editing the core env logic.
• Rely on documented APIs and configs
  • Use sensor and controller configs from Isaac Lab docs instead of ad‑hoc code (e.g., camera configuration, controller cfgs).⁹⁶⁴
  • For Franka + RGB‑D, follow the way existing tasks configure cameras and arm assets, then only specialize what GR00T needs (e.g., image resolution, camera pose).
• Incremental integration
  • Step 1: Validate task with random/zero policy.
  • Step 2: Replace the random policy with a simple scripted or PID controller using the same action interface.
  • Step 3: Plug in GR00T via your policy wrapper, keeping the same env API.
  • This reduces the surface area where both Isaac Lab and GR00T code can fail simultaneously.

Useful starting links:

• Isaac Lab main documentation: project, tasks, and workflows.¹
• Template generator: external project + internal task creation, including --new usage and validation scripts.²
• Tutorials: step‑by‑step custom environments and task examples.³
• Available environments: reference implementations of RL/IL tasks.⁴
• Isaac GR00T model/card and repo for understanding expected inputs/outputs when designing your adapters.¹⁰¹¹⁸

Footnotes

1. https://isaac-sim.github.io/IsaacLab/ ↩ ↩² ↩³ ↩⁴ ↩⁵

2. https://isaac-sim.github.io/IsaacLab/main/source/overview/own-project/template.html ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸

3. https://isaac-sim.github.io/IsaacLab/main/source/tutorials/index.html ↩ ↩² ↩³ ↩⁴

4. https://isaac-sim.github.io/IsaacLab/main/source/overview/environments.html ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

5. https://github.com/isaac-sim/IsaacLabEvalTasks ↩ ↩²

6. https://isaac-sim.github.io/IsaacLab/main/source/api/index.html ↩ ↩² ↩³

7. https://github.com/isaac-sim/IsaacLab/discussions/1632 ↩ ↩²

8. https://github.com/NVIDIA/Isaac-GR00T/blob/main/README.md ↩ ↩²

9. https://docs.isaacsim.omniverse.nvidia.com/4.2.0/ros_tutorials/tutorial_ros_camera.html ↩

10. https://huggingface.co/nvidia/GR00T-N1.5-3B ↩

11. https://github.com/NVIDIA/Isaac-GR00T ↩