runtime/env_manager/rbs_gym/launch/train.launch.py

from launch import LaunchDescription
from launch.actions import (
    DeclareLaunchArgument,
    IncludeLaunchDescription,
    OpaqueFunction,
    SetEnvironmentVariable,
    TimerAction
)
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.substitutions import FindPackageShare
from launch_ros.actions import Node
import os
from os import cpu_count
from ament_index_python.packages import get_package_share_directory
import yaml
import xacro

def launch_setup(context, *args, **kwargs):
    # Initialize Arguments
    robot_type = LaunchConfiguration("robot_type")
    # General arguments
    with_gripper_condition = LaunchConfiguration("with_gripper")
    description_package = LaunchConfiguration("description_package")
    description_file = LaunchConfiguration("description_file")
    use_moveit = LaunchConfiguration("use_moveit")
    moveit_config_package = LaunchConfiguration("moveit_config_package")
    moveit_config_file = LaunchConfiguration("moveit_config_file")
    use_sim_time = LaunchConfiguration("use_sim_time")
    scene_config_file = LaunchConfiguration("scene_config_file").perform(context)

    ee_link_name = LaunchConfiguration("ee_link_name").perform(context)
    base_link_name = LaunchConfiguration("base_link_name").perform(context)

    control_space = LaunchConfiguration("control_space").perform(context)
    control_strategy = LaunchConfiguration("control_strategy").perform(context)

    interactive = LaunchConfiguration("interactive").perform(context)

    # training arguments
    env = LaunchConfiguration("env")
    algo = LaunchConfiguration("algo")
    hyperparams = LaunchConfiguration("hyperparams")
    n_timesteps = LaunchConfiguration("n_timesteps")
    num_threads = LaunchConfiguration("num_threads")
    seed = LaunchConfiguration("seed")
    trained_agent = LaunchConfiguration("trained_agent")
    save_freq = LaunchConfiguration("save_freq")
    save_replay_buffer = LaunchConfiguration("save_replay_buffer")
    preload_replay_buffer = LaunchConfiguration("preload_replay_buffer")
    log_folder = LaunchConfiguration("log_folder")
    tensorboard_log = LaunchConfiguration("tensorboard_log")
    log_interval = LaunchConfiguration("log_interval")
    uuid = LaunchConfiguration("uuid")
    eval_freq = LaunchConfiguration("eval_freq")
    eval_episodes = LaunchConfiguration("eval_episodes")
    verbose = LaunchConfiguration("verbose")
    truncate_last_trajectory = LaunchConfiguration("truncate_last_trajectory")
    use_sim_time = LaunchConfiguration("use_sim_time")
    log_level = LaunchConfiguration("log_level")
    env_kwargs = LaunchConfiguration("env_kwargs")

    track = LaunchConfiguration("track")

    description_package_abs_path = get_package_share_directory(
        description_package.perform(context)
    )

    simulation_controllers = os.path.join(
        description_package_abs_path, "config", "controllers.yaml"
    )


    xacro_file = os.path.join(
        description_package_abs_path,
        "urdf",
        description_file.perform(context),
    )

    xacro_config_file = f"{description_package_abs_path}/config/xacro_args.yaml"


    # TODO: hide this to another place
    # Load xacro_args
    def param_constructor(loader, node, local_vars):
        value = loader.construct_scalar(node)
        return LaunchConfiguration(value).perform(
            local_vars.get("context", "Launch context if not defined")
        )

    def variable_constructor(loader, node, local_vars):
        value = loader.construct_scalar(node)
        return local_vars.get(value, f"Variable '{value}' not found")

    def load_xacro_args(yaml_file, local_vars):
        # Get valut from ros2 argument
        yaml.add_constructor(
            "!param", lambda loader, node: param_constructor(loader, node, local_vars)
        )

        # Get value from local variable in this code
        # The local variable should be initialized before the loader was called
        yaml.add_constructor(
            "!variable",
            lambda loader, node: variable_constructor(loader, node, local_vars),
        )

        with open(yaml_file, "r") as file:
            return yaml.load(file, Loader=yaml.FullLoader)

    mappings_data = load_xacro_args(xacro_config_file, locals())

    robot_description_doc = xacro.process_file(xacro_file, mappings=mappings_data)

    robot_description_semantic_content = ""

    if use_moveit.perform(context) == "true":
        srdf_config_file = f"{description_package_abs_path}/config/srdf_xacro_args.yaml"
        srdf_file = os.path.join(
            get_package_share_directory(moveit_config_package.perform(context)),
            "srdf",
            moveit_config_file.perform(context),
        )
        srdf_mappings = load_xacro_args(srdf_config_file, locals())
        robot_description_semantic_content = xacro.process_file(srdf_file, mappings=srdf_mappings)
        robot_description_semantic_content = robot_description_semantic_content.toprettyxml(indent=" ")
        control_space = "joint"
        control_strategy = "position"
        interactive = "false"


    robot_description_content = robot_description_doc.toprettyxml(indent="  ")
    robot_description = {"robot_description": robot_description_content}

    single_robot_setup = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            [
                PathJoinSubstitution(
                    [FindPackageShare("rbs_bringup"), "launch", "rbs_robot.launch.py"]
                )
            ]
        ),
        launch_arguments={
            "with_gripper": with_gripper_condition,
            "controllers_file": simulation_controllers,
            "robot_type": robot_type,
            "description_package": description_package,
            "description_file": description_file,
            "robot_name": robot_type,
            "use_moveit": use_moveit,
            "moveit_config_package": moveit_config_package,
            "moveit_config_file": moveit_config_file,
            "use_sim_time": use_sim_time,
            "use_controllers": "true",
            "robot_description": robot_description_content,
            "robot_description_semantic": robot_description_semantic_content,
            "base_link_name": base_link_name,
            "ee_link_name": ee_link_name,
            "control_space": control_space,
            "control_strategy": control_strategy,
            "interactive_control": interactive,
        }.items(),
    )

    args = [
        "--env",
        env,
        "--env-kwargs",
        env_kwargs,
        "--algo",
        algo,
        "--hyperparams",
        hyperparams,
        "--n-timesteps",
        n_timesteps,
        "--num-threads",
        num_threads,
        "--seed",
        seed,
        "--trained-agent",
        trained_agent,
        "--save-freq",
        save_freq,
        "--save-replay-buffer",
        save_replay_buffer,
        "--preload-replay-buffer",
        preload_replay_buffer,
        "--log-folder",
        log_folder,
        "--tensorboard-log",
        tensorboard_log,
        "--log-interval",
        log_interval,
        "--uuid",
        uuid,
        "--eval-freq",
        eval_freq,
        "--eval-episodes",
        eval_episodes,
        "--verbose",
        verbose,
        "--track",
        track,
        "--truncate-last-trajectory",
        truncate_last_trajectory,
        "--ros-args",
        "--log-level",
        log_level,
    ]

    clock_bridge = Node(
        package='ros_gz_bridge',
        executable='parameter_bridge',
        arguments=['/clock@rosgraph_msgs/msg/Clock[ignition.msgs.Clock'],
        output='screen')

    rl_task = Node(
        package="rbs_gym",
        executable="train",
        output="log",
        arguments=args,
        parameters=[{"use_sim_time": True}]
    )


    delay_robot_control_stack = TimerAction(
        period=20.0,
        actions=[single_robot_setup]
    )
    
    nodes_to_start = [
        # env,
        rl_task,
        clock_bridge,
        delay_robot_control_stack
    ]
    return nodes_to_start


def generate_launch_description():
    declared_arguments = []
    declared_arguments.append(
        DeclareLaunchArgument(
            "robot_type",
            description="Type of robot by name",
            choices=[
                "rbs_arm",
                "ar4",
                "ur3",
                "ur3e",
                "ur5",
                "ur5e",
                "ur10",
                "ur10e",
                "ur16e",
            ],
            default_value="rbs_arm",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "description_package",
            default_value="rbs_arm",
            description="Description package with robot URDF/XACRO files. Usually the argument \
        is not set, it enables use of a custom description.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "description_file",
            default_value="rbs_arm_modular.xacro",
            description="URDF/XACRO description file with the robot.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "robot_name",
            default_value="arm0",
            description="Name for robot, used to apply namespace for specific robot in multirobot setup",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "moveit_config_package",
            default_value="rbs_arm",
            description="MoveIt config package with robot SRDF/XACRO files. Usually the argument \
        is not set, it enables use of a custom moveit config.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "moveit_config_file",
            default_value="rbs_arm.srdf.xacro",
            description="MoveIt SRDF/XACRO description file with the robot.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "use_sim_time",
            default_value="true",
            description="Make MoveIt to use simulation time.\
            This is needed for the trajectory planing in simulation.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "with_gripper", default_value="true", description="With gripper or not?"
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "use_moveit", default_value="false", description="Launch moveit?"
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "launch_perception", default_value="false", description="Launch perception?"
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "use_controllers",
            default_value="true",
            description="Launch controllers?",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "scene_config_file",
            default_value="",
            description="Path to a scene configuration file",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "ee_link_name",
            default_value="",
            description="End effector name of robot arm",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "base_link_name",
            default_value="",
            description="Base link name if robot arm",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "control_space",
            default_value="task",
            choices=["task", "joint"],
            description="Specify the control space for the robot (e.g., task space).",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "control_strategy",
            default_value="position",
            choices=["position", "velocity", "effort"],
            description="Specify the control strategy (e.g., position control).",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "interactive",
            default_value="true",
            description="Wheter to run the motion_control_handle controller",
        ),
    )
    # training arguments
    declared_arguments.append(
        DeclareLaunchArgument(
            "env",
            default_value="Reach-Gazebo-v0",
            description="Environment ID",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "env_kwargs",
            default_value="",
            description="Optional keyword argument to pass to the env constructor.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "vec_env",
            default_value="dummy",
            description="Type of VecEnv to use (dummy or subproc).",
        ))
        # Algorithm and training
    declared_arguments.append(
        DeclareLaunchArgument(
            "algo",
            default_value="sac",
            description="RL algorithm to use during the training.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "n_timesteps",
            default_value="-1",
            description="Overwrite the number of timesteps.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "hyperparams",
            default_value="",
            description="Optional RL hyperparameter overwrite (e.g. learning_rate:0.01 train_freq:10).",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "num_threads",
            default_value="-1",
            description="Number of threads for PyTorch (-1 to use default).",
        ))
        # Continue training an already trained agent
    declared_arguments.append(
        DeclareLaunchArgument(
            "trained_agent",
            default_value="",
            description="Path to a pretrained agent to continue training.",
        ))
        # Random seed
    declared_arguments.append(
        DeclareLaunchArgument(
            "seed",
            default_value="-1",
            description="Random generator seed.",
        ))
        # Saving of model
    declared_arguments.append(
        DeclareLaunchArgument(
            "save_freq",
            default_value="10000",
            description="Save the model every n steps (if negative, no checkpoint).",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "save_replay_buffer",
            default_value="False",
            description="Save the replay buffer too (when applicable).",
        ))
        # Pre-load a replay buffer and start training on it
    declared_arguments.append(
        DeclareLaunchArgument(
            "preload_replay_buffer",
            default_value="",
            description="Path to a replay buffer that should be preloaded before starting the training process.",
        ))
        # Logging
    declared_arguments.append(
        DeclareLaunchArgument(
            "log_folder",
            default_value="logs",
            description="Path to the log directory.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "tensorboard_log",
            default_value="tensorboard_logs",
            description="Tensorboard log dir.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "log_interval",
            default_value="-1",
            description="Override log interval (default: -1, no change).",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "uuid",
            default_value="False",
            description="Ensure that the run has a unique ID.",
        ))
        # Evaluation
    declared_arguments.append(
        DeclareLaunchArgument(
            "eval_freq",
            default_value="-1",
            description="Evaluate the agent every n steps (if negative, no evaluation).",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "eval_episodes",
            default_value="5",
            description="Number of episodes to use for evaluation.",
        ))
        # Verbosity
    declared_arguments.append(
        DeclareLaunchArgument(
            "verbose",
            default_value="1",
            description="Verbose mode (0: no output, 1: INFO).",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "truncate_last_trajectory",
            default_value="True",
            description="When using HER with online sampling the last trajectory in the replay buffer will be truncated after) reloading the replay buffer."
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "log_level",
            default_value="error",
            description="The level of logging that is applied to all ROS 2 nodes launched by this script.",
        ))
    declared_arguments.append(
        DeclareLaunchArgument(
            "track",
            default_value="true",
            description="The level of logging that is applied to all ROS 2 nodes launched by this script.",
        ))

    env_variables = [
        SetEnvironmentVariable(name="OMP_DYNAMIC", value="TRUE"),
        SetEnvironmentVariable(name="OMP_NUM_THREADS", value=str(cpu_count() // 2))
    ]

    return LaunchDescription(declared_arguments + [OpaqueFunction(function=launch_setup)] + env_variables)