Environment Setup and Validation#

On this page we briefly describe the environment used in this example workflow and validate that we can load it in Isaac Lab.

Docker Container: Base (see Docker Containers for more details)

./docker/run_docker.sh

Environment Description#

Step-by-Step Breakdown#

1. Interact with the Asset and Device Registry

background = self.asset_registry.get_asset_by_name("galileo_locomanip")()
pick_up_object = self.asset_registry.get_asset_by_name(args_cli.object)()
blue_sorting_bin = self.asset_registry.get_asset_by_name("blue_sorting_bin")()
embodiment = self.asset_registry.get_asset_by_name(args_cli.embodiment)(enable_cameras=args_cli.enable_cameras)

teleop_device = self.device_registry.get_device_by_name(args_cli.teleop_device)()

Here, we’re selecting the specific pieces we need for our locomanipulation task: the Galileo arena as our background environment, an object to pick up, a blue sorting bin as our goal location, and the G1 embodiment. The AssetRegistry and DeviceRegistry have been initialized in the ExampleEnvironmentBase class. See Assets Design for details on asset architecture.

2. Position the Objects

pick_up_object.set_initial_pose(
    Pose(
        position_xyz=(0.5785, 0.18, 0.0707),
        rotation_wxyz=(0.0, 0.0, 1.0, 0.0),
    )
)
blue_sorting_bin.set_initial_pose(
    Pose(
        position_xyz=(-0.2450, -1.6272, -0.2641),
        rotation_wxyz=(0.0, 0.0, 0.0, 1.0),
    )
)

Before we create the scene, we need to place our objects in the right locations. These initial poses are currently set manually to create an achievable task.

3. Compose the Scene

scene = Scene(assets=[background, pick_up_object, blue_sorting_bin])

Now we bring everything together into a IsaacLab-Arena scene. See Scene Design for scene composition details.

4. Create the Locomanip Pick and Place Task

task = G1LocomanipPickAndPlaceTask(pick_up_object, blue_sorting_bin, background),

The G1LocomanipPickAndPlaceTask encapsulates the task’s goal of the environment: pick up the specified object and place it in the blue sorting bin.

The task knows about the key objects involved (what to pick, where to place it, and the environment context) and will provide rewards, compute observations, and determine when the episode should end. See Tasks Design for task creation details.

5. Create the IsaacLab Arena Environment

isaaclab_arena_environment = IsaacLabArenaEnvironment(
    name=self.name,
    embodiment=embodiment,
    scene=scene,
    task=task,
    teleop_device=teleop_device,
)

Finally, we assemble all the pieces into a complete, runnable environment. The IsaacLabArenaEnvironment is the top-level container that connects your embodiment (the robot), the scene (the world) and the task (the objective). See Environment Design for environment composition details.

Step 1: Download a test dataset#

To run a robot in the environment we need some recorded demonstration data that can be fed to the robot to control its actions. We download a pre-recorded dataset from Hugging Face:

hf download \
    nvidia/Arena-G1-Loco-Manipulation-Task \
    arena_g1_loco_manipulation_dataset_generated_small.hdf5 \
    --repo-type dataset \
    --local-dir $DATASET_DIR

Step 2: Validate Environment with Demo Replay#

Replay the downloaded dataset to verify the environment setup

python isaaclab_arena/scripts/replay_demos.py \
  --device cpu \
  --enable_cameras \
  --dataset_file ${DATASET_DIR}/arena_g1_loco_manipulation_dataset_generated_small.hdf5 \
  galileo_g1_locomanip_pick_and_place \
  --object brown_box \
  --embodiment g1_wbc_pink

Note

The downloaded dataset was generated using CPU device physics, therefore the replay uses --device cpu to ensure reproducibility.

You should see the G1 robot replaying the generated demonstrations, performing box pick and place task in the Galileo lab environment.

IsaacLab Arena G1 Locomanip Pick and Place Task View#