from launch import LaunchDescription from launch.actions import ( DeclareLaunchArgument, IncludeLaunchDescription, OpaqueFunction, 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 def launch_setup(context, *args, **kwargs): # Initialize Arguments robot_type = LaunchConfiguration("robot_type") # General arguments with_gripper_condition = LaunchConfiguration("with_gripper") controllers_file = LaunchConfiguration("controllers_file") cartesian_controllers = LaunchConfiguration("cartesian_controllers") description_package = LaunchConfiguration("description_package") description_file = LaunchConfiguration("description_file") robot_name = LaunchConfiguration("robot_name") start_joint_controller = LaunchConfiguration("start_joint_controller") initial_joint_controller = LaunchConfiguration("initial_joint_controller") launch_simulation = LaunchConfiguration("launch_sim") launch_moveit = LaunchConfiguration("launch_moveit") launch_task_planner = LaunchConfiguration("launch_task_planner") launch_perception = LaunchConfiguration("launch_perception") moveit_config_package = LaunchConfiguration("moveit_config_package") moveit_config_file = LaunchConfiguration("moveit_config_file") use_sim_time = LaunchConfiguration("use_sim_time") sim_gazebo = LaunchConfiguration("sim_gazebo") hardware = LaunchConfiguration("hardware") env_manager = LaunchConfiguration("env_manager") launch_controllers = LaunchConfiguration("launch_controllers") gripper_name = LaunchConfiguration("gripper_name") # training arguments env = LaunchConfiguration("env") env_kwargs = LaunchConfiguration("env_kwargs") algo = LaunchConfiguration("algo") hyperparams = LaunchConfiguration("hyperparams") n_timesteps = LaunchConfiguration("n_timesteps") num_threads = LaunchConfiguration("num_threads") seed = LaunchConfiguration("seed") 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_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") sampler = LaunchConfiguration("sampler") pruner = LaunchConfiguration("pruner") n_trials = LaunchConfiguration("n_trials") n_startup_trials = LaunchConfiguration("n_startup_trials") n_evaluations = LaunchConfiguration("n_evaluations") n_jobs = LaunchConfiguration("n_jobs") storage = LaunchConfiguration("storage") study_name = LaunchConfiguration("study_name") sim_gazebo = LaunchConfiguration("sim_gazebo") launch_simulation = LaunchConfiguration("launch_sim") initial_joint_controllers_file_path = os.path.join( get_package_share_directory('rbs_arm'), 'config', 'rbs_arm0_controllers.yaml' ) single_robot_setup = IncludeLaunchDescription( PythonLaunchDescriptionSource([ PathJoinSubstitution([ FindPackageShare('rbs_bringup'), "launch", "rbs_robot.launch.py" ]) ]), launch_arguments={ "env_manager": env_manager, "with_gripper": with_gripper_condition, "gripper_name": gripper_name, "controllers_file": controllers_file, "robot_type": robot_type, "controllers_file": initial_joint_controllers_file_path, "cartesian_controllers": cartesian_controllers, "description_package": description_package, "description_file": description_file, "robot_name": robot_name, "start_joint_controller": start_joint_controller, "initial_joint_controller": initial_joint_controller, "launch_simulation": launch_simulation, "launch_moveit": launch_moveit, "launch_task_planner": launch_task_planner, "launch_perception": launch_perception, "moveit_config_package": moveit_config_package, "moveit_config_file": moveit_config_file, "use_sim_time": use_sim_time, "sim_gazebo": sim_gazebo, "hardware": hardware, "launch_controllers": launch_controllers, # "gazebo_gui": gazebo_gui }.items() ) args = [ "--env", env, "--env-kwargs", env_kwargs, "--algo", algo, "--seed", seed, "--num-threads", num_threads, "--n-timesteps", n_timesteps, "--preload-replay-buffer", preload_replay_buffer, "--log-folder", log_folder, "--tensorboard-log", tensorboard_log, "--log-interval", log_interval, "--uuid", uuid, "--optimize-hyperparameters", "True", "--sampler", sampler, "--pruner", pruner, "--n-trials", n_trials, "--n-startup-trials", n_startup_trials, "--n-evaluations", n_evaluations, "--n-jobs", n_jobs, "--storage", storage, "--study-name", study_name, "--eval-episodes", eval_episodes, "--verbose", verbose, "--truncate-last-trajectory", truncate_last_trajectory, "--ros-args", "--log-level", log_level, ] rl_task = Node( package="rbs_gym", executable="train.py", output="log", arguments = args, parameters=[{"use_sim_time": True}] ) delay_robot_control_stack = TimerAction( period=10.0, actions=[single_robot_setup] ) nodes_to_start = [ rl_task, 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","ur3", "ur3e", "ur5", "ur5e", "ur10", "ur10e", "ur16e"], default_value="rbs_arm", ) ) # General arguments declared_arguments.append( DeclareLaunchArgument( "controllers_file", default_value="rbs_arm_controllers_gazebosim.yaml", description="YAML file with the controllers configuration.", ) ) 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( "start_joint_controller", default_value="false", description="Enable headless mode for robot control", ) ) declared_arguments.append( DeclareLaunchArgument( "initial_joint_controller", default_value="joint_trajectory_controller", description="Robot controller to start.", ) ) 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( "gripper_name", default_value="rbs_gripper", choices=["rbs_gripper", ""], description="choose gripper by name (leave empty if hasn't)", ) ) declared_arguments.append( DeclareLaunchArgument("with_gripper", default_value="true", description="With gripper or not?") ) declared_arguments.append( DeclareLaunchArgument("sim_gazebo", default_value="true", description="Gazebo Simulation") ) declared_arguments.append( DeclareLaunchArgument("env_manager", default_value="false", description="Launch env_manager?") ) declared_arguments.append( DeclareLaunchArgument("launch_sim", default_value="true", description="Launch simulator (Gazebo)?\ Most general arg") ) declared_arguments.append( DeclareLaunchArgument("launch_moveit", default_value="false", description="Launch moveit?") ) declared_arguments.append( DeclareLaunchArgument("launch_perception", default_value="false", description="Launch perception?") ) declared_arguments.append( DeclareLaunchArgument("launch_task_planner", default_value="false", description="Launch task_planner?") ) declared_arguments.append( DeclareLaunchArgument("cartesian_controllers", default_value="true", description="Load cartesian\ controllers?") ) declared_arguments.append( DeclareLaunchArgument("hardware", choices=["gazebo", "mock"], default_value="gazebo", description="Choose your harware_interface") ) declared_arguments.append( DeclareLaunchArgument("launch_controllers", default_value="true", description="Launch controllers?") ) declared_arguments.append( DeclareLaunchArgument("gazebo_gui", default_value="true", description="Launch gazebo with gui?") ) # 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="84", 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.", )) declared_arguments.append( DeclareLaunchArgument( "sampler", default_value="tpe", description="Sampler to use when optimizing hyperparameters (random, tpe or skopt).", )) declared_arguments.append( DeclareLaunchArgument( "pruner", default_value="median", description="Pruner to use when optimizing hyperparameters (halving, median or none).", )) declared_arguments.append( DeclareLaunchArgument( "n_trials", default_value="10", description="Number of trials for optimizing hyperparameters.", )) declared_arguments.append( DeclareLaunchArgument( "n_startup_trials", default_value="5", description="Number of trials before using optuna sampler.", )) declared_arguments.append( DeclareLaunchArgument( "n_evaluations", default_value="2", description="Number of evaluations for hyperparameter optimization.", )) declared_arguments.append( DeclareLaunchArgument( "n_jobs", default_value="1", description="Number of parallel jobs when optimizing hyperparameters.", )) declared_arguments.append( DeclareLaunchArgument( "storage", default_value="", description="Database storage path if distributed optimization should be used.", )) declared_arguments.append( DeclareLaunchArgument( "study_name", default_value="", description="Study name for distributed optimization.", )) # 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).", )) # HER specifics 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.", )) # 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)])