eval_utils

find_start_point(base_vel)

Find the first point where the base velocity is non-zero. This is used to skip the initial part of the dataset where the robot is not moving.

Source code in OmniGibson/omnigibson/learning/utils/eval_utils.py

def find_start_point(base_vel):
    """
    Find the first point where the base velocity is non-zero.
    This is used to skip the initial part of the dataset where the robot is not moving.
    """
    start_idx = np.where(np.linalg.norm(base_vel, axis=-1) > 1e-5)[0]
    if len(start_idx) == 0:
        return 0
    return min(start_idx[0], 500)  # Limit to the first 100 points to avoid long initial periods

flatten_obs_dict(obs, parent_key='')

Process the observation dictionary by recursively flattening the keys. so obs["robot_r1"]["camera"]["rgb"] will become obs["robot_r1:::rgb"].

Source code in OmniGibson/omnigibson/learning/utils/eval_utils.py

def flatten_obs_dict(obs: dict, parent_key: str = "") -> dict:
    """
    Process the observation dictionary by recursively flattening the keys.
    so obs["robot_r1"]["camera"]["rgb"] will become obs["robot_r1::camera:::rgb"].
    """
    processed_obs = {}
    for key, value in obs.items():
        new_key = f"{parent_key}::{key}" if parent_key else key
        if isinstance(value, dict):
            processed_obs.update(flatten_obs_dict(value, parent_key=new_key))
        else:
            processed_obs[new_key] = value
    return processed_obs

generate_basic_environment_config(task_name, task_cfg)

Generate a basic environment configuration

Parameters:

Name	Type	Description	Default
task_name	str	Name of the task	required
task_cfg		Dictionary of task config	required

Returns:

Type	Description
dict	Environment configuration

Source code in OmniGibson/omnigibson/learning/utils/eval_utils.py

def generate_basic_environment_config(task_name, task_cfg):
    """
    Generate a basic environment configuration

    Args:
        task_name (str): Name of the task
        task_cfg: Dictionary of task config

    Returns:
        dict: Environment configuration
    """
    cfg = {
        "env": {
            "action_frequency": 30,
            "rendering_frequency": 30,
            "physics_frequency": 120,
        },
        "scene": {
            "type": "InteractiveTraversableScene",
            "scene_model": task_cfg["scene_model"],
            "load_room_types": None,
            "load_room_instances": task_cfg.get("load_room_instances", None),
            "include_robots": False,
        },
        "task": {
            "type": "BehaviorTask",
            "activity_name": task_name,
            "activity_definition_id": 0,
            "activity_instance_id": 0,
            "predefined_problem": None,
            "online_object_sampling": False,
            "debug_object_sampling": False,
            "highlight_task_relevant_objects": False,
            "termination_config": {
                "max_steps": 5000,
            },
            "reward_config": {
                "r_potential": 1.0,
            },
            "include_obs": False,
        },
    }
    return cfg