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wrappers

DefaultWrapper

Bases: EnvironmentWrapper

Parameters:

Name Type Description Default
env Environment

The environment to wrap.

 required
Source code in OmniGibson/omnigibson/learning/wrappers/default_wrapper.py 
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class DefaultWrapper(EnvironmentWrapper):
    """
    Args:
        env (og.Environment): The environment to wrap.
    """

    def __init__(self, env: Environment):
        super().__init__(env=env)
        # Note that from eval.py we only set rgb modality, here we include more (depth + seg_instance_id)
        # Here, we change the camera resolution and head camera aperture to match the one we used in data collection
        robot = env.robots[0]
        # Update robot sensors:
        for camera_id, camera_name in ROBOT_CAMERA_NAMES["R1Pro"].items():
            sensor_name = camera_name.split("::")[1]
            if camera_id == "head":
                robot.sensors[sensor_name].horizontal_aperture = 40.0
                robot.sensors[sensor_name].image_height = HEAD_RESOLUTION[0]
                robot.sensors[sensor_name].image_width = HEAD_RESOLUTION[1]
            else:
                robot.sensors[sensor_name].image_height = WRIST_RESOLUTION[0]
                robot.sensors[sensor_name].image_width = WRIST_RESOLUTION[1]
            # add depth and segmentation
            robot.sensors[sensor_name].add_modality("depth_linear")
            robot.sensors[sensor_name].add_modality("seg_semantic")
            robot.sensors[sensor_name].add_modality("seg_instance_id")
        # reload observation space
        env.load_observation_space()
        logger.info("Reloaded observation space!")

HeavyRobotWrapper

Bases: EnvironmentWrapper

Parameters:

Name Type Description Default
env Environment

The environment to wrap.

 required
Source code in OmniGibson/omnigibson/learning/wrappers/heavy_robot_wrapper.py 
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class HeavyRobotWrapper(EnvironmentWrapper):
    """
    Args:
        env (og.Environment): The environment to wrap.
    """

    def __init__(self, env: Environment):
        super().__init__(env=env)
        # Here, we modify the robot observation to  use 224 * 224 resolution
        # For a complete list of available modalities, see VisionSensor.ALL_MODALITIES
        # We also change the robot base mass to 250kg to match the configuration during data collection.
        robot = env.robots[0]
        with og.sim.stopped():
            robot.base_footprint_link.mass = 250.0  # increase base mass to 250kg
        # Update robot sensors:
        for camera_id, camera_name in ROBOT_CAMERA_NAMES["R1Pro"].items():
            sensor_name = camera_name.split("::")[1]
            if camera_id == "head":
                robot.sensors[sensor_name].horizontal_aperture = 40.0  # this is what we used in data collection
            robot.sensors[sensor_name].image_height = 224
            robot.sensors[sensor_name].image_width = 224
        # reload observation space
        env.load_observation_space()
        logger.info("Reloaded observation space!")

RGBLowResWrapper

Bases: EnvironmentWrapper

Parameters:

Name Type Description Default
env Environment

The environment to wrap.

 required
Source code in OmniGibson/omnigibson/learning/wrappers/rgb_low_res_wrapper.py 
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class RGBLowResWrapper(EnvironmentWrapper):
    """
    Args:
        env (og.Environment): The environment to wrap.
    """

    def __init__(self, env: Environment):
        super().__init__(env=env)
        # Here, we modify the robot observation to use 224 * 224 resolution
        # For a complete list of available modalities, see VisionSensor.ALL_MODALITIES
        robot = env.robots[0]
        for camera_id, camera_name in ROBOT_CAMERA_NAMES["R1Pro"].items():
            sensor_name = camera_name.split("::")[1]
            if camera_id == "head":
                robot.sensors[sensor_name].horizontal_aperture = 40.0  # this is what we used in data collection
            robot.sensors[sensor_name].image_height = 224
            robot.sensors[sensor_name].image_width = 224
        # reload observation space
        env.load_observation_space()
        logger.info("Reloaded observation space!")

RichObservationWrapper

Bases: EnvironmentWrapper

Parameters:

Name Type Description Default
env Environment

The environment to wrap.

 required
Source code in OmniGibson/omnigibson/learning/wrappers/rich_obs_wrapper.py 
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class RichObservationWrapper(EnvironmentWrapper):
    """
    Args:
        env (og.Environment): The environment to wrap.
    """

    def __init__(self, env: Environment):
        super().__init__(env=env)
        # Note that from eval.py we already set the robot to include rgb + depth + seg_instance_id modalities
        robot = env.robots[0]
        # Here, we change the camera resolution and head camera aperture to match the one we used in data collection
        for camera_id, camera_name in ROBOT_CAMERA_NAMES["R1Pro"].items():
            sensor_name = camera_name.split("::")[1]
            if camera_id == "head":
                robot.sensors[sensor_name].horizontal_aperture = 40.0
                robot.sensors[sensor_name].image_height = HEAD_RESOLUTION[0]
                robot.sensors[sensor_name].image_width = HEAD_RESOLUTION[1]
            else:
                robot.sensors[sensor_name].image_height = WRIST_RESOLUTION[0]
                robot.sensors[sensor_name].image_width = WRIST_RESOLUTION[1]
            # Here, we modify the robot observation to include normal and flow modalities
            # For a complete list of available modalities, see VisionSensor.ALL_MODALITIES
            robot.sensors[sensor_name].add_modality("normal")
            robot.sensors[sensor_name].add_modality("flow")
        # reload observation space
        env.load_observation_space()
        # we also set task to include obs
        env.task._include_obs = True
        logger.info("Reloaded observation space!")

    def step(self, action, n_render_iterations=1):
        """
        By default, run the normal environment step() function

        Args:
            action (th.tensor): action to take in environment
            n_render_iterations (int): Number of rendering iterations to use before returning observations

        Returns:
            4-tuple:
                - (dict) observations from the environment
                - (float) reward from the environment
                - (bool) whether the current episode is terminated
                - (bool) whether the current episode is truncated
                - (dict) misc information
        """
        obs, reward, terminated, truncated, info = self.env.step(action, n_render_iterations=n_render_iterations)
        # Now, query for some additional privileged task info
        obs["task"] = self.env.task.get_obs(self.env)
        return obs, reward, terminated, truncated, info

    def reset(self):
        # Note that we need to also add additional observations in reset() because the returned observation will be passed into policy
        ret = self.env.reset()
        ret[0]["task"] = self.env.task.get_obs(self.env)
        return ret

step(action, n_render_iterations=1)

By default, run the normal environment step() function

Parameters:

Name Type Description Default
action tensor

action to take in environment

 required
n_render_iterations int

Number of rendering iterations to use before returning observations

 1

Returns:

Type Description
4 - tuple
  • (dict) observations from the environment
  • (float) reward from the environment
  • (bool) whether the current episode is terminated
  • (bool) whether the current episode is truncated
  • (dict) misc information
Source code in OmniGibson/omnigibson/learning/wrappers/rich_obs_wrapper.py 
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def step(self, action, n_render_iterations=1):
    """
    By default, run the normal environment step() function

    Args:
        action (th.tensor): action to take in environment
        n_render_iterations (int): Number of rendering iterations to use before returning observations

    Returns:
        4-tuple:
            - (dict) observations from the environment
            - (float) reward from the environment
            - (bool) whether the current episode is terminated
            - (bool) whether the current episode is truncated
            - (dict) misc information
    """
    obs, reward, terminated, truncated, info = self.env.step(action, n_render_iterations=n_render_iterations)
    # Now, query for some additional privileged task info
    obs["task"] = self.env.task.get_obs(self.env)
    return obs, reward, terminated, truncated, info