Bill Finger
ea4ae0ed69
- Rename default controllers file `rbs_arm0_controllers.yaml` to `controllers.yaml` across all launch files - Enhance `runtime.launch.py` by adding imports (`URDF_parser`, `ControllerManager`, `yaml`) and new arguments (`scene_config_file`, `ee_link_name`, `base_link_name`) - Implement xacro argument loading via YAML - Configure `robot_builder` to save controllers to `controllers.yaml` based on URDF parsing - Add `control.launch.py` in `rbs_bringup` for dynamic controller spawning based on strategies/configurations - Add `rbs_bringup.launch.py` as a unified entry point for single robot setup, initializing core settings like gripper, robot type, and controllers - Remove `launch_env.launch.py` - Remove `multi_robot.launch.py` - Enhance `rbs_robot.launch.py` with `ee_link_name` and `base_link_name` arguments for end-effector and base link customization - Add `ee_link_name` and `base_link_name` to `skills.launch.py` for flexible robot setup in skills - Update `mtp_jtc_cart.cpp` with `base_link` and `robot_ee_link` parameters for KDL chain retrieval - Remove `single_robot.launch.py` for simplified single robot deployment
519 lines
18 KiB
Python
519 lines
18 KiB
Python
from launch import LaunchDescription
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from launch.actions import (
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DeclareLaunchArgument,
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IncludeLaunchDescription,
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OpaqueFunction,
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TimerAction
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)
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from launch.launch_description_sources import PythonLaunchDescriptionSource
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from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
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from launch_ros.substitutions import FindPackageShare
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from launch_ros.actions import Node
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import os
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from os import cpu_count
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from ament_index_python.packages import get_package_share_directory
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def launch_setup(context, *args, **kwargs):
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# Initialize Arguments
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robot_type = LaunchConfiguration("robot_type")
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# General arguments
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with_gripper_condition = LaunchConfiguration("with_gripper")
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controllers_file = LaunchConfiguration("controllers_file")
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cartesian_controllers = LaunchConfiguration("cartesian_controllers")
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description_package = LaunchConfiguration("description_package")
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description_file = LaunchConfiguration("description_file")
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robot_name = LaunchConfiguration("robot_name")
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start_joint_controller = LaunchConfiguration("start_joint_controller")
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initial_joint_controller = LaunchConfiguration("initial_joint_controller")
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launch_simulation = LaunchConfiguration("launch_sim")
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launch_moveit = LaunchConfiguration("launch_moveit")
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launch_task_planner = LaunchConfiguration("launch_task_planner")
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launch_perception = LaunchConfiguration("launch_perception")
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moveit_config_package = LaunchConfiguration("moveit_config_package")
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moveit_config_file = LaunchConfiguration("moveit_config_file")
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use_sim_time = LaunchConfiguration("use_sim_time")
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sim_gazebo = LaunchConfiguration("sim_gazebo")
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hardware = LaunchConfiguration("hardware")
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env_manager = LaunchConfiguration("env_manager")
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launch_controllers = LaunchConfiguration("launch_controllers")
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gripper_name = LaunchConfiguration("gripper_name")
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# training arguments
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env = LaunchConfiguration("env")
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env_kwargs = LaunchConfiguration("env_kwargs")
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algo = LaunchConfiguration("algo")
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hyperparams = LaunchConfiguration("hyperparams")
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n_timesteps = LaunchConfiguration("n_timesteps")
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num_threads = LaunchConfiguration("num_threads")
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seed = LaunchConfiguration("seed")
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preload_replay_buffer = LaunchConfiguration("preload_replay_buffer")
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log_folder = LaunchConfiguration("log_folder")
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tensorboard_log = LaunchConfiguration("tensorboard_log")
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log_interval = LaunchConfiguration("log_interval")
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uuid = LaunchConfiguration("uuid")
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eval_episodes = LaunchConfiguration("eval_episodes")
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verbose = LaunchConfiguration("verbose")
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truncate_last_trajectory = LaunchConfiguration("truncate_last_trajectory")
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use_sim_time = LaunchConfiguration("use_sim_time")
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log_level = LaunchConfiguration("log_level")
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sampler = LaunchConfiguration("sampler")
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pruner = LaunchConfiguration("pruner")
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n_trials = LaunchConfiguration("n_trials")
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n_startup_trials = LaunchConfiguration("n_startup_trials")
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n_evaluations = LaunchConfiguration("n_evaluations")
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n_jobs = LaunchConfiguration("n_jobs")
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storage = LaunchConfiguration("storage")
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study_name = LaunchConfiguration("study_name")
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sim_gazebo = LaunchConfiguration("sim_gazebo")
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launch_simulation = LaunchConfiguration("launch_sim")
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initial_joint_controllers_file_path = os.path.join(
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get_package_share_directory('rbs_arm'), 'config', 'controllers.yaml'
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)
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single_robot_setup = IncludeLaunchDescription(
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PythonLaunchDescriptionSource([
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PathJoinSubstitution([
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FindPackageShare('rbs_bringup'),
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"launch",
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"rbs_robot.launch.py"
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])
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]),
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launch_arguments={
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"env_manager": env_manager,
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"with_gripper": with_gripper_condition,
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"gripper_name": gripper_name,
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"controllers_file": controllers_file,
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"robot_type": robot_type,
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"controllers_file": initial_joint_controllers_file_path,
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"cartesian_controllers": cartesian_controllers,
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"description_package": description_package,
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"description_file": description_file,
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"robot_name": robot_name,
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"start_joint_controller": start_joint_controller,
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"initial_joint_controller": initial_joint_controller,
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"launch_simulation": launch_simulation,
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"launch_moveit": launch_moveit,
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"launch_task_planner": launch_task_planner,
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"launch_perception": launch_perception,
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"moveit_config_package": moveit_config_package,
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"moveit_config_file": moveit_config_file,
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"use_sim_time": use_sim_time,
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"sim_gazebo": sim_gazebo,
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"hardware": hardware,
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"launch_controllers": launch_controllers,
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# "gazebo_gui": gazebo_gui
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}.items()
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)
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args = [
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"--env",
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env,
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"--env-kwargs",
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env_kwargs,
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"--algo",
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algo,
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"--seed",
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seed,
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"--num-threads",
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num_threads,
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"--n-timesteps",
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n_timesteps,
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"--preload-replay-buffer",
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preload_replay_buffer,
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"--log-folder",
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log_folder,
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"--tensorboard-log",
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tensorboard_log,
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"--log-interval",
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log_interval,
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"--uuid",
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uuid,
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"--optimize-hyperparameters",
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"True",
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"--sampler",
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sampler,
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"--pruner",
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pruner,
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"--n-trials",
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n_trials,
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"--n-startup-trials",
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n_startup_trials,
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"--n-evaluations",
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n_evaluations,
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"--n-jobs",
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n_jobs,
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"--storage",
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storage,
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"--study-name",
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study_name,
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"--eval-episodes",
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eval_episodes,
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"--verbose",
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verbose,
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"--truncate-last-trajectory",
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truncate_last_trajectory,
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"--ros-args",
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"--log-level",
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log_level,
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]
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rl_task = Node(
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package="rbs_gym",
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executable="train",
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output="log",
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arguments = args,
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parameters=[{"use_sim_time": True}]
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)
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delay_robot_control_stack = TimerAction(
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period=10.0,
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actions=[single_robot_setup]
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)
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nodes_to_start = [
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rl_task,
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delay_robot_control_stack
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]
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return nodes_to_start
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def generate_launch_description():
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declared_arguments = []
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declared_arguments.append(
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DeclareLaunchArgument(
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"robot_type",
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description="Type of robot by name",
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choices=["rbs_arm","ur3", "ur3e", "ur5", "ur5e", "ur10", "ur10e", "ur16e"],
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default_value="rbs_arm",
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)
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)
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# General arguments
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declared_arguments.append(
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DeclareLaunchArgument(
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"controllers_file",
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default_value="controllers_gazebosim.yaml",
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description="YAML file with the controllers configuration.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"description_package",
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default_value="rbs_arm",
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description="Description package with robot URDF/XACRO files. Usually the argument \
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is not set, it enables use of a custom description.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"description_file",
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default_value="rbs_arm_modular.xacro",
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description="URDF/XACRO description file with the robot.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"robot_name",
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default_value="arm0",
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description="Name for robot, used to apply namespace for specific robot in multirobot setup",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"start_joint_controller",
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default_value="false",
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description="Enable headless mode for robot control",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"initial_joint_controller",
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default_value="joint_trajectory_controller",
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description="Robot controller to start.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"moveit_config_package",
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default_value="rbs_arm",
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description="MoveIt config package with robot SRDF/XACRO files. Usually the argument \
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is not set, it enables use of a custom moveit config.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"moveit_config_file",
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default_value="rbs_arm.srdf.xacro",
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description="MoveIt SRDF/XACRO description file with the robot.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"use_sim_time",
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default_value="true",
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description="Make MoveIt to use simulation time.\
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This is needed for the trajectory planing in simulation.",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument(
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"gripper_name",
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default_value="rbs_gripper",
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choices=["rbs_gripper", ""],
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description="choose gripper by name (leave empty if hasn't)",
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)
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)
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declared_arguments.append(
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DeclareLaunchArgument("with_gripper",
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default_value="true",
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description="With gripper or not?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("sim_gazebo",
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default_value="true",
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description="Gazebo Simulation")
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)
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declared_arguments.append(
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DeclareLaunchArgument("env_manager",
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default_value="false",
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description="Launch env_manager?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("launch_sim",
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default_value="true",
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description="Launch simulator (Gazebo)?\
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Most general arg")
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)
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declared_arguments.append(
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DeclareLaunchArgument("launch_moveit",
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default_value="false",
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description="Launch moveit?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("launch_perception",
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default_value="false",
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description="Launch perception?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("launch_task_planner",
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default_value="false",
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description="Launch task_planner?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("cartesian_controllers",
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default_value="true",
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description="Load cartesian\
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controllers?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("hardware",
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choices=["gazebo", "mock"],
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default_value="gazebo",
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description="Choose your harware_interface")
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)
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declared_arguments.append(
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DeclareLaunchArgument("launch_controllers",
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default_value="true",
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description="Launch controllers?")
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)
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declared_arguments.append(
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DeclareLaunchArgument("gazebo_gui",
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default_value="true",
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description="Launch gazebo with gui?")
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)
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# training arguments
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declared_arguments.append(
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DeclareLaunchArgument(
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"env",
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default_value="Reach-Gazebo-v0",
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description="Environment ID",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"env_kwargs",
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default_value="",
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description="Optional keyword argument to pass to the env constructor.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"vec_env",
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default_value="dummy",
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description="Type of VecEnv to use (dummy or subproc).",
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))
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# Algorithm and training
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declared_arguments.append(
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DeclareLaunchArgument(
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"algo",
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default_value="sac",
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description="RL algorithm to use during the training.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"n_timesteps",
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default_value="-1",
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description="Overwrite the number of timesteps.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"hyperparams",
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default_value="",
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description="Optional RL hyperparameter overwrite (e.g. learning_rate:0.01 train_freq:10).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"num_threads",
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default_value="-1",
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description="Number of threads for PyTorch (-1 to use default).",
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))
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# Continue training an already trained agent
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declared_arguments.append(
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DeclareLaunchArgument(
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"trained_agent",
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default_value="",
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description="Path to a pretrained agent to continue training.",
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))
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# Random seed
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declared_arguments.append(
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DeclareLaunchArgument(
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"seed",
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default_value="84",
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description="Random generator seed.",
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))
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# Saving of model
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declared_arguments.append(
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DeclareLaunchArgument(
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"save_freq",
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default_value="10000",
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description="Save the model every n steps (if negative, no checkpoint).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"save_replay_buffer",
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default_value="False",
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description="Save the replay buffer too (when applicable).",
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))
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# Pre-load a replay buffer and start training on it
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declared_arguments.append(
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DeclareLaunchArgument(
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"preload_replay_buffer",
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default_value="",
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description="Path to a replay buffer that should be preloaded before starting the training process.",
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))
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# Logging
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declared_arguments.append(
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DeclareLaunchArgument(
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"log_folder",
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default_value="logs",
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description="Path to the log directory.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"tensorboard_log",
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default_value="tensorboard_logs",
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description="Tensorboard log dir.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"log_interval",
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default_value="-1",
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description="Override log interval (default: -1, no change).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"uuid",
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default_value="False",
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description="Ensure that the run has a unique ID.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"sampler",
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default_value="tpe",
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description="Sampler to use when optimizing hyperparameters (random, tpe or skopt).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"pruner",
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default_value="median",
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description="Pruner to use when optimizing hyperparameters (halving, median or none).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"n_trials",
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default_value="10",
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description="Number of trials for optimizing hyperparameters.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"n_startup_trials",
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default_value="5",
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description="Number of trials before using optuna sampler.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"n_evaluations",
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default_value="2",
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description="Number of evaluations for hyperparameter optimization.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"n_jobs",
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default_value="1",
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description="Number of parallel jobs when optimizing hyperparameters.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"storage",
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default_value="",
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description="Database storage path if distributed optimization should be used.",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"study_name",
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default_value="",
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description="Study name for distributed optimization.",
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))
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# Evaluation
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declared_arguments.append(
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DeclareLaunchArgument(
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"eval_freq",
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default_value="-1",
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description="Evaluate the agent every n steps (if negative, no evaluation).",
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))
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declared_arguments.append(
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DeclareLaunchArgument(
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"eval_episodes",
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default_value="5",
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description="Number of episodes to use for evaluation.",
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))
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# Verbosity
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declared_arguments.append(
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DeclareLaunchArgument(
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"verbose",
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default_value="1",
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description="Verbose mode (0: no output, 1: INFO).",
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))
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# HER specifics
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declared_arguments.append(
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DeclareLaunchArgument(
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"truncate_last_trajectory",
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default_value="True",
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description="When using HER with online sampling the last trajectory in the replay buffer will be truncated after) reloading the replay buffer."
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)),
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declared_arguments.append(
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DeclareLaunchArgument(
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"log_level",
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default_value="error",
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description="The level of logging that is applied to all ROS 2 nodes launched by this script.",
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))
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# env_variables = [
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# SetEnvironmentVariable(name="OMP_DYNAMIC", value="TRUE"),
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# SetEnvironmentVariable(name="OMP_NUM_THREADS", value=str(cpu_count() // 2))
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# ]
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return LaunchDescription(declared_arguments + [OpaqueFunction(function=launch_setup)])
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