Manipulation exampleΒΆ

This example provides a wider overview of the functionalities currently implemented in gym-ignition.

The code reported below is taken from the example panda_pick_and_place.py. It shows the following functionalities:

  • Download models from Ignition Fuel.

  • Insert and remove models during runtime.

  • Exploit the high-level Panda helper class to insert the manipulator.

  • Enable custom C++ controllers (scenario::controllers::ComputedTorqueFixedBase).

  • Detect contacts and read contact wrenches.

  • Exploit InverseKinematicsNLP.

https://user-images.githubusercontent.com/469199/99905096-f29a0f80-2cce-11eb-9f1c-002f6c887bc6.png

Tip

This example can be the starting point to develop manipulation environments for robot learning. Visit the gym-ignition page for more information about how to wrap this code in the resources provided by gym_ignition.

Note

This is an illustrative example. A compliant joint controller is used to move the manipulator, it does not perform any trajectory planning and it does not avoid self collisions, which are not enabled in the simulation by default. There are cube positions where the joint configuration changes a lot from the previous, generating an abrupt movement of the manipulator. When these situations occur, grasping might fail.

import enum
import time
from functools import partial
from typing import List

import gym_ignition
import gym_ignition_environments
import numpy as np
from gym_ignition.context.gazebo import controllers
from gym_ignition.rbd import conversions
from gym_ignition.rbd.idyntree import inverse_kinematics_nlp
from scipy.spatial.transform import Rotation as R

from scenario import core as scenario_core
from scenario import gazebo as scenario_gazebo

# Configure verbosity
scenario_gazebo.set_verbosity(scenario_gazebo.Verbosity_error)

# Configure numpy output
np.set_printoptions(precision=4, suppress=True)


def add_panda_controller(
    panda: gym_ignition_environments.models.panda.Panda, controller_period: float
) -> None:

    # Set the controller period
    assert panda.set_controller_period(period=controller_period)

    # Increase the max effort of the fingers
    panda.get_joint(
        joint_name="panda_finger_joint1"
    ).to_gazebo().set_max_generalized_force(max_force=500.0)
    panda.get_joint(
        joint_name="panda_finger_joint2"
    ).to_gazebo().set_max_generalized_force(max_force=500.0)

    # Insert the ComputedTorqueFixedBase controller
    assert panda.to_gazebo().insert_model_plugin(
        *controllers.ComputedTorqueFixedBase(
            kp=[100.0] * (panda.dofs() - 2) + [10000.0] * 2,
            ki=[0.0] * panda.dofs(),
            kd=[17.5] * (panda.dofs() - 2) + [100.0] * 2,
            urdf=panda.get_model_file(),
            joints=panda.joint_names(),
        ).args()
    )

    # Initialize the controller to the current state
    assert panda.set_joint_position_targets(panda.joint_positions())
    assert panda.set_joint_velocity_targets(panda.joint_velocities())
    assert panda.set_joint_acceleration_targets(panda.joint_accelerations())


def get_panda_ik(
    panda: gym_ignition_environments.models.panda.Panda, optimized_joints: List[str]
) -> inverse_kinematics_nlp.InverseKinematicsNLP:

    # Create IK
    ik = inverse_kinematics_nlp.InverseKinematicsNLP(
        urdf_filename=panda.get_model_file(),
        considered_joints=optimized_joints,
        joint_serialization=panda.joint_names(),
    )

    # Initialize IK
    ik.initialize(
        verbosity=1,
        floating_base=False,
        cost_tolerance=1e-8,
        constraints_tolerance=1e-8,
        base_frame=panda.base_frame(),
    )

    # Set the current configuration
    ik.set_current_robot_configuration(
        base_position=np.array(panda.base_position()),
        base_quaternion=np.array(panda.base_orientation()),
        joint_configuration=np.array(panda.joint_positions()),
    )

    # Add the cartesian target of the end effector
    end_effector = "end_effector_frame"
    ik.add_target(
        frame_name=end_effector,
        target_type=inverse_kinematics_nlp.TargetType.POSE,
        as_constraint=False,
    )

    return ik


def insert_bucket(world: scenario_gazebo.World) -> scenario_gazebo.Model:

    # Insert objects from Fuel
    uri = lambda org, name: f"https://fuel.ignitionrobotics.org/{org}/models/{name}"

    # Download the cube SDF file
    bucket_sdf = scenario_gazebo.get_model_file_from_fuel(
        uri=uri(
            org="GoogleResearch",
            name="Threshold_Basket_Natural_Finish_Fabric_Liner_Small",
        ),
        use_cache=False,
    )

    # Assign a custom name to the model
    model_name = "bucket"

    # Insert the model
    assert world.insert_model(
        bucket_sdf, scenario_core.Pose([0.68, 0, 1.02], [1.0, 0, 0, 1]), model_name
    )

    # Return the model
    return world.get_model(model_name=model_name)


def insert_table(world: scenario_gazebo.World) -> scenario_gazebo.Model:

    # Insert objects from Fuel
    uri = lambda org, name: f"https://fuel.ignitionrobotics.org/{org}/models/{name}"

    # Download the cube SDF file
    bucket_sdf = scenario_gazebo.get_model_file_from_fuel(
        uri=uri(org="OpenRobotics", name="Table"), use_cache=False
    )

    # Assign a custom name to the model
    model_name = "table"

    # Insert the model
    assert world.insert_model(bucket_sdf, scenario_core.Pose_identity(), model_name)

    # Return the model
    return world.get_model(model_name=model_name)


def insert_cube_in_operating_area(
    world: scenario_gazebo.World,
) -> scenario_gazebo.Model:

    # Insert objects from Fuel
    uri = lambda org, name: f"https://fuel.ignitionrobotics.org/{org}/models/{name}"

    # Download the cube SDF file
    cube_sdf = scenario_gazebo.get_model_file_from_fuel(
        uri=uri(org="openrobotics", name="wood cube 5cm"), use_cache=False
    )

    # Sample a random position
    random_position = np.random.uniform(low=[0.2, -0.3, 1.01], high=[0.4, 0.3, 1.01])

    # Get a unique name
    model_name = gym_ignition.utils.scenario.get_unique_model_name(
        world=world, model_name="cube"
    )

    # Insert the model
    assert world.insert_model(
        cube_sdf, scenario_core.Pose(random_position, [1.0, 0, 0, 0]), model_name
    )

    # Return the model
    return world.get_model(model_name=model_name)


def solve_ik(
    target_position: np.ndarray,
    target_orientation: np.ndarray,
    ik: inverse_kinematics_nlp.InverseKinematicsNLP,
) -> np.ndarray:

    quat_xyzw = R.from_euler(seq="y", angles=90, degrees=True).as_quat()

    ik.update_transform_target(
        target_name=ik.get_active_target_names()[0],
        position=target_position,
        quaternion=conversions.Quaternion.to_wxyz(xyzw=quat_xyzw),
    )

    # Run the IK
    ik.solve()

    return ik.get_reduced_solution().joint_configuration


def end_effector_reached(
    position: np.array,
    end_effector_link: scenario_core.Link,
    max_error_pos: float = 0.01,
    max_error_vel: float = 0.5,
    mask: np.ndarray = np.array([1.0, 1.0, 1.0]),
) -> bool:

    masked_target = mask * position
    masked_current = mask * np.array(end_effector_link.position())

    return (
        np.linalg.norm(masked_current - masked_target) < max_error_pos
        and np.linalg.norm(end_effector_link.world_linear_velocity()) < max_error_vel
    )


def get_unload_position(bucket: scenario_core.Model) -> np.ndarray:

    return bucket.base_position() + np.array([0, 0, 0.3])


class FingersAction(enum.Enum):

    OPEN = enum.auto()
    CLOSE = enum.auto()


def move_fingers(
    panda: gym_ignition_environments.models.panda.Panda, action: FingersAction
) -> None:

    # Get the joints of the fingers
    finger1 = panda.get_joint(joint_name="panda_finger_joint1")
    finger2 = panda.get_joint(joint_name="panda_finger_joint2")

    if action is FingersAction.OPEN:
        finger1.set_position_target(position=finger1.position_limit().max)
        finger2.set_position_target(position=finger2.position_limit().max)

    if action is FingersAction.CLOSE:
        finger1.set_position_target(position=finger1.position_limit().min)
        finger2.set_position_target(position=finger2.position_limit().min)


# ====================
# INITIALIZE THE WORLD
# ====================

# Get the simulator and the world
gazebo, world = gym_ignition.utils.scenario.init_gazebo_sim(
    step_size=0.001, real_time_factor=2.0, steps_per_run=1
)

# Open the GUI
gazebo.gui()
time.sleep(3)
gazebo.run(paused=True)

# Insert the Panda manipulator
panda = gym_ignition_environments.models.panda.Panda(
    world=world, position=[-0.1, 0, 1.0]
)

# Disable joint velocity limits
_ = [j.to_gazebo().set_velocity_limit(1_000) for j in panda.joints()]

# Enable contacts only for the finger links
panda.get_link("panda_leftfinger").to_gazebo().enable_contact_detection(True)
panda.get_link("panda_rightfinger").to_gazebo().enable_contact_detection(True)

# Process model insertion in the simulation
gazebo.run(paused=True)

# Monkey patch the class with finger helpers
panda.open_fingers = partial(move_fingers, panda=panda, action=FingersAction.OPEN)
panda.close_fingers = partial(move_fingers, panda=panda, action=FingersAction.CLOSE)

# Add a custom joint controller to the panda
add_panda_controller(panda=panda, controller_period=gazebo.step_size())

# Populate the world
table = insert_table(world=world)
bucket = insert_bucket(world=world)
gazebo.run(paused=True)

# Create and configure IK for the panda
ik_joints = [
    j.name() for j in panda.joints() if j.type is not scenario_core.JointType_fixed
]
ik = get_panda_ik(panda=panda, optimized_joints=ik_joints)

# Get some manipulator links
finger_left = panda.get_link(link_name="panda_leftfinger")
finger_right = panda.get_link(link_name="panda_rightfinger")
end_effector_frame = panda.get_link(link_name="end_effector_frame")

while True:

    # Insert a new cube
    cube = insert_cube_in_operating_area(world=world)
    gazebo.run(paused=True)

    # =========================
    # PHASE 1: Go over the cube
    # =========================

    print("Hovering")

    # Position over the cube
    position_over_cube = np.array(cube.base_position()) + np.array([0, 0, 0.4])

    # Get the joint configuration that brings the EE over the cube
    over_joint_configuration = solve_ik(
        target_position=position_over_cube,
        target_orientation=np.array(cube.base_orientation()),
        ik=ik,
    )

    # Set the joint references
    assert panda.set_joint_position_targets(over_joint_configuration, ik_joints)

    # Open the fingers
    panda.open_fingers()

    # Run the simulation until the EE reached the desired position
    while not end_effector_reached(
        position=position_over_cube,
        end_effector_link=end_effector_frame,
        max_error_pos=0.05,
        max_error_vel=0.5,
    ):
        gazebo.run()

    # Wait a bit more
    [gazebo.run() for _ in range(500)]

    # =======================
    # PHASE 2: Reach the cube
    # =======================

    print("Reaching")

    # Get the joint configuration that brings the EE to the cube
    over_joint_configuration = solve_ik(
        target_position=np.array(cube.base_position()) + np.array([0, 0, 0.04]),
        target_orientation=np.array(cube.base_orientation()),
        ik=ik,
    )

    # Set the joint references
    assert panda.set_joint_position_targets(over_joint_configuration, ik_joints)
    panda.open_fingers()

    # Run the simulation until the EE reached the desired position
    while not end_effector_reached(
        position=np.array(cube.base_position()) + np.array([0, 0, 0.04]),
        end_effector_link=end_effector_frame,
    ):

        gazebo.run()

    # Wait a bit more
    [gazebo.run() for _ in range(500)]

    # =======================
    # PHASE 3: Grasp the cube
    # =======================

    print("Grasping")

    # Close the fingers
    panda.close_fingers()

    # Detect a graps reading the contact wrenches of the finger links
    while not (
        np.linalg.norm(finger_left.contact_wrench()) >= 50.0
        and np.linalg.norm(finger_right.contact_wrench()) >= 50.0
    ):
        gazebo.run()

    # =============
    # PHASE 4: Lift
    # =============

    print("Lifting")

    # Position over the cube
    position_over_cube = np.array(cube.base_position()) + np.array([0, 0, 0.4])

    # Get the joint configuration that brings the EE over the cube
    over_joint_configuration = solve_ik(
        target_position=position_over_cube,
        target_orientation=np.array(cube.base_orientation()),
        ik=ik,
    )

    # Set the joint references
    assert panda.set_joint_position_targets(over_joint_configuration, ik_joints)

    # Run the simulation until the EE reached the desired position
    while not end_effector_reached(
        position=position_over_cube,
        end_effector_link=end_effector_frame,
        max_error_pos=0.1,
        max_error_vel=0.5,
    ):
        gazebo.run()

    # Wait a bit more
    [gazebo.run() for _ in range(500)]

    # =====================================
    # PHASE 5: Place the cube in the bucket
    # =====================================

    print("Dropping")

    # Get the joint configuration that brings the EE over the bucket
    unload_joint_configuration = solve_ik(
        target_position=get_unload_position(bucket=bucket),
        target_orientation=np.array([0, 1.0, 0, 0]),
        ik=ik,
    )

    # Set the joint references
    assert panda.set_joint_position_targets(unload_joint_configuration, ik_joints)

    # Run the simulation until the EE reached the desired position
    while not end_effector_reached(
        position=get_unload_position(bucket=bucket)
        + np.random.uniform(low=-0.05, high=0.05, size=3),
        end_effector_link=end_effector_frame,
        max_error_pos=0.01,
        max_error_vel=0.1,
        mask=np.array([1, 1, 0]),
    ):

        gazebo.run()

    # Open the fingers
    panda.open_fingers()

    # Wait that both fingers are in not contact (with the cube)
    while finger_left.in_contact() or finger_right.in_contact():
        gazebo.run()

    # Wait a bit more
    [gazebo.run() for _ in range(500)]

    # Remove the cube
    world.remove_model(model_name=cube.name())

# It is always a good practice to close the simulator.
# In this case it is not required since above there is an infinite loop.
# gazebo.close()

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