Case Study: A Dive Into IsaacLab

Basic File Hierarchy

Most of the environment related config files are in source/extensions folder.

The folder source/standalone provides a set of demo programs, tools, and the entry script to the manager-based environments that can be invoked user.

Environment Definitions

Task definitions

The tasks are defined in the source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based folder.

The interface to these RL frameworks are defined in source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/wrappers

We define the task-specific configuration and parameters.

A change compared to LeggedGym is that the registration of task is moved to each robot's own __init__.py file.

The environment is defiend as RslRlVecEnvWrapper, where it wraps around the ManagerBasedRLEnv class.

For config file, it follows this inheritance logic

ManagerBasedEnvCfg -> ManagerBasedRLEnvCfg -> LocomotionVelocityRoughEnvCfg -> G1RoughEnvCfg

ManagerBasedEnvCfg

it defines the following config pieces:

viewer: ViewerCfg -

sim: SimulationCfg -

ui_window_class_type -

decimation -

scene: InteractiveSceneCfg -

observations - observation space settings

actions - action space settings

events: DefaultEventManagerCfg - handles reset events

ManagerBasedRLEnvCfg

is_finite_horizon: bool

episode_length_s: float - duration of an episode in seconds, episode_length_steps = ceil(episode_length_s / (decimation_rate * physics_time_step))

rewards: RewardManager

terminations: TerminationManager

curriculum: CurriculumManager

commands: CommandManager

LocomotionVelocityRoughEnvCfg

It instanciates the managers upon initialization, and provides a custom __post_init__ routine

Inference Logic

Similar to LeggedGym, we start off from play.py.

We first create the simulation environment env with the gym.make() function.

The env returned is an instance of ManagerBasedRLEnv

Then, we wrap it inside the RslRlVecEnvWrapper, which provides the following methods

get_observations()

reset()

step()

episode_length_buf

Step() logic is mostly implemented in ManagerBasedRLEnv.

1. Process the actions.
2. Perform physics stepping.
3. Perform rendering if gui is enabled.
4. Update the environment counters and compute the rewards and terminations.
5. Reset the environments that terminated.
6. Compute the observations.
7. Return the observations, rewards, resets and extras.

Fixing the robot in-place

Sometimes when debugging, we wish to pin the robot in a fixed location. This can be achieved by changing the following attribute:

class EnvCfg():
    def __post_init__(self):
        super().__post_init__()
        # assume self.scene.robot is already defined
        self.scene.robot.spawn.articulation_props.fix_root_link = True

Adding Camera View

import omni.ui as ui

...

def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene):
    # im = plt.imshow(np.zeros((480, 640, 1), dtype=np.float32))
    # # set scale to 1.0
    # im.set_clim(0, 10)
    # plt.colorbar(im)
    # cv2.namedWindow("Depth Image", cv2.WINDOW_NORMAL)
    # cv2.resizeWindow("Depth Image", 640, 480)

    depth_window = ui.Window("Depth Image", width=640, height=480)
    with depth_window.frame:
        bytes_provider = ui.ByteImageProvider()
        bytes_provider.set_bytes_data(np.zeros((480, 640, 1), dtype=np.float32).flatten().data, [480, 640], ui.TextureFormat.R32_SFLOAT)
        depth_image_widget = ui.ImageWithProvider(bytes_provider)  # Create an image widget


    while simulation_app.is_running():
        # print information from the sensors
        # print("-------------------------------")
        # print(scene["camera"])
        # print("Received shape of rgb   image: ", scene["camera"].data.output["rgb"].shape)

        # visualize depth image
        depth_image = scene["camera"].data.output["distance_to_image_plane"]
        depth_image = depth_image[0, ...].cpu().numpy().astype(np.float32)

        depth_image /= 1000.0
        
        # im.set_data(depth_image)
        # plt.pause(0.001)  # Allow the plot to update

        # cv2.imshow("Depth Image", depth_image)
        # cv2.waitKey(1)
        bytes_provider.set_bytes_data(depth_image.flatten().data, [480, 640], ui.TextureFormat.R32_SFLOAT)

        print("depth_image.shape: ", depth_image.shape)

        # print("Received shape of depth image: ", scene["camera"].data.output["distance_to_image_plane"].shape)
        # print("-------------------------------")
        # print(scene["height_scanner"])
        # print("Received max height value: ", torch.max(scene["height_scanner"].data.ray_hits_w[..., -1]).item())
        # print("-------------------------------")
        # print(scene["contact_forces"])
        # print("Received max contact force of: ", torch.max(scene["contact_forces"].data.net_forces_w).item())

Last updated 7 months ago

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