ros_gazebo_gym.task_envs.panda.panda_reach

An ROS Panda reach gymnasium environment.

Panda reach environment
Observation space:

As the panda environment inherits from the gym.GoalEnv class, the observation space is a dictionary.

Type: Dict

  • observation (numpy.ndarray): The current end-effector pose, joint positions and joint velocities.

  • desired_goal (numpy.ndarray): The desired end-effector pose.

  • achieved_goal (numpy.ndarray): The achieved end-effector pose.

Action space:

The action space of the panda environment is dependent on the control type and whether the gripper is loaded. The following action spaces are available:

Joint trajectory control:
Type: Box(7)

  • panda_joint1 (float): The position of the first joint.

  • panda_joint2 (float): The position of the second joint.

  • panda_joint3 (float): The position of the third joint.

  • panda_joint4 (float): The position of the fourth joint.

  • panda_joint5 (float): The position of the fifth joint.

  • panda_joint6 (float): The position of the sixth joint.

  • panda_joint7 (float): The position of the seventh joint.

Joint position control:
Type: Box(7)

  • panda_joint1 (float): The position of the first joint.

  • panda_joint2 (float): The position of the second joint.

  • panda_joint3 (float): The position of the third joint.

  • panda_joint4 (float): The position of the fourth joint.

  • panda_joint5 (float): The position of the fifth joint.

  • panda_joint6 (float): The position of the sixth joint.

  • panda_joint7 (float): The position of the seventh joint.

Joint effort control:
Type: Box(7)

  • panda_joint1 (float): The effort of the first joint.

  • panda_joint2 (float): The effort of the second joint.

  • panda_joint3 (float): The effort of the third joint.

  • panda_joint4 (float): The effort of the fourth joint.

  • panda_joint5 (float): The effort of the fifth joint.

  • panda_joint6 (float): The effort of the sixth joint.

  • panda_joint7 (float): The effort of the seventh joint.

End-effector position control:
Type: Box(7)

  • x (float): The x position of the end-effector.

  • y (float): The y position of the end-effector.

  • z (float): The z position of the end-effector.

  • rx (float): The x component of the quaternion orientation of the end-effector.

  • ry (float): The y component of the quaternion orientation of the end-effector.

  • rz (float): The z component of the quaternion orientation of the end-effector.

  • rw (float): The w component of the quaternion orientation of the end-effector.

If the gripper is loaded, the action space is extended with the following dimensions:

Type: Box(2)

  • gripper_width (float): The width of the gripper - only if the gripper is loaded.

  • gripper_max_effort (float): The maximum effort of the gripper - only if the gripper is loaded.

Attention

The gripper width is ignored when the grasping parameter is set to true in the task environment config file. or when the gripper_max_effort is set to a value greater than zero.

Episode termination:

The episode terminates when the end-effector is within a certain distance of the goal position. The distance is defined by the distance_threshold parameter in the task environment configuration file. If the hold_samples parameter is greater than zero, the episode will terminate after hold_samples consecutive samples are within the distance_threshold. The episode will also terminate if the maximum number of samples is reached.

Environment Goal:

In this environment the agent has to learn to move the panda robot to a given goal position. It was based on the FetchReach-v2 gymnasium environment.

Reward function:

The reward function is defined as the negative of the Euclidean distance between the end-effector and the goal position. If the positive_reward parameter is set to true, the absolute value of the reward is returned:

\[reward = -\sqrt{(x_{ee} - x_{goal})^2 + (y_{ee} - y_{goal})^2 + (z_{ee} - z_{goal})^2}\]
Initialization:

The environment is initialized by loading the Panda robot model and setting its initial position and orientation. The environment parameters can be set in the configuration file located at ros_gazebo_gym/task_envs/panda/config/panda_reach.yaml.

Environment step return:

In addition to the observations, the reward, and a termination and truncation boolean, the environment also returns an info dictionary:

[observation, reward, termination, truncation, info_dict]

The info dictionary contains the following information:

  • reference: The reference position (x,y,z) that the Panda Reach is tracking (i.e. the goal position).

  • state_of_interest: The state that should track the reference (SOI) (i.e. the end-effector position).

  • reference_error: The error between SOI and the reference (i.e. the error between the end-effector position and the goal position).

Configuration

The configuration files for this environment are found in the panda task environment config folder.

Module Contents

Classes

PandaReachEnv

Class that provides all the methods used for the algorithm training.

Attributes

CONNECTION_TIMEOUT

MOVEIT_GET_RANDOM_JOINT_POSITIONS_TOPIC

MOVEIT_SET_JOINT_POSITIONS_TOPIC

MOVEIT_GET_RANDOM_EE_POSE_TOPIC

MOVEIT_ADD_PLANE_TOPIC

SET_FRANKA_MODEL_CONFIGURATION_TOPIC

VALID_EE_CONTROL_JOINTS

CONFIG_FILE_PATH

PANDA_REST_CONFIGURATION

LOG_STEP_DEBUG_INFO

AVAILABLE_HAND_COMMANDS

ros_gazebo_gym.task_envs.panda.panda_reach.CONNECTION_TIMEOUT = 5[source]
ros_gazebo_gym.task_envs.panda.panda_reach.MOVEIT_GET_RANDOM_JOINT_POSITIONS_TOPIC = 'panda_moveit_planner_server/get_random_joint_positions'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.MOVEIT_SET_JOINT_POSITIONS_TOPIC = 'panda_moveit_planner_server/set_joint_positions'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.MOVEIT_GET_RANDOM_EE_POSE_TOPIC = 'panda_moveit_planner_server/get_random_ee_pose'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.MOVEIT_ADD_PLANE_TOPIC = 'panda_moveit_planner_server/planning_scene/add_plane'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.SET_FRANKA_MODEL_CONFIGURATION_TOPIC = 'set_franka_model_configuration'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.VALID_EE_CONTROL_JOINTS = ['x', 'y', 'z', 'rx', 'ry', 'rz', 'rw'][source]
ros_gazebo_gym.task_envs.panda.panda_reach.CONFIG_FILE_PATH = 'config/panda_reach.yaml'[source]
ros_gazebo_gym.task_envs.panda.panda_reach.PANDA_REST_CONFIGURATION[source]
ros_gazebo_gym.task_envs.panda.panda_reach.LOG_STEP_DEBUG_INFO = False[source]
ros_gazebo_gym.task_envs.panda.panda_reach.AVAILABLE_HAND_COMMANDS = ['gripper_width', 'gripper_max_effort'][source]
class ros_gazebo_gym.task_envs.panda.panda_reach.PandaReachEnv(control_type='effort', positive_reward=False, config_path=CONFIG_FILE_PATH, gazebo_world_launch_file='start_reach_world.launch', visualize=None, action_space_dtype=np.float64, observation_space_dtype=np.float64)[source]

Bases: ros_gazebo_gym.robot_envs.panda_env.PandaEnv, gymnasium.utils.EzPickle

Class that provides all the methods used for the algorithm training.

action_space

Gym action space object.

Type:

gym.spaces.box.Box

observation_space

Gym observation space object.

Type:

gym.spaces.dict.Dict

goal

The current goal.

Type:

geometry_msgs.PoseStamped

Initializes a Panda Task Environment.

Parameters:

  • control_Type (str, optional) – The type of control you want to use for the panda robot (i.e. hand and arm). Options are: trajectory, position, effort or end_effector. Defaults to effort.

  • positive_reward (bool, optional) – Whether you want to use a positive reward instead of a negative reward. Defaults to False.

  • config_path (str, optional) – Path where the environment configuration value are found. The path is resolved relative to the panda_reach class file.

  • gazebo_world_launch_file (str, optional) – Name of the launch file that loads the gazebo world. Currently only the launch files inside the panda_gazebo package are supported. Defaults to start_reach_world.launch.

  • visualize (bool, optional) – Whether you want to show the RViz visualization. Defaults to None meaning the task configuration file values will be used.

  • action_space_dtype (union[numpy.dtype, str], optional) – The data type of the action space. Defaults to np.float64.

  • observation_space_dtype (union[numpy.dtype, str], optional) – The data type of the observation space. Defaults to np.float64.

Important

In this environment, the joint trajectory control is not implemented yet for multiple waypoints. This is because the action space only contains one waypoint. The set_arm_joint_trajectory method, however, already accepts multiple waypoints. As a result, task environment can be easily extended to work with multiple waypoints by modifying the PandaReachEnv~._create_action_space method.