if __name__ == "__main__":
from itertools import count
from pyrobolearn.simulators import Bullet
from pyrobolearn.worlds import BasicWorld
# Create simulator
sim = Bullet()
# create world
world = BasicWorld(sim)
# create robot
robots = []
for _ in range(30):
x, y = np.random.uniform(low=-1, high=1, size=2)
robot = world.load_robot(Kilobot, position=(x, y, 0))
robots.append(robot)
# print information about the robot
robots[0].print_info()
# Position control using sliders
# robots[0].add_joint_slider()
# run simulator
for _ in count():
# robots[0].update_joint_slider()
for robot in robots:
robot.drive(5)
world.step(sleep_dt=1. / 240)
def __getattr__(self, item):
"""The Gym Env have the same methods and attributes as the PRL Env."""
return getattr(self.env, item)
# Tests
if __name__ == '__main__':
from pyrobolearn.simulators import Bullet
import time
# create simulator
sim = Bullet()
# create world
world = BasicWorld(sim)
robot = world.load_robot('coman', fixed_base=True)
# create states
states = State()
# create rewards
reward = Reward()
# create Env
env = Env(world, states, reward)
# dummy action
action = Action()
# run the environment for n steps
for _ in range(10000):
from itertools import count
from pyrobolearn.simulators import Bullet
from pyrobolearn.worlds import BasicWorld
from pyrobolearn.robots import KukaIIWA, Body
# Create simulator
sim = Bullet()
# create world
world = BasicWorld(sim)
# create robot
robot = KukaIIWA(sim)
robot.print_info()
world.load_robot(robot)
# define useful variables for IK
dt = 1. / 240
link_id = robot.get_end_effector_ids(end_effector=0)
joint_ids = robot.joints # actuated joint
damping = 0.01 # for damped-least-squares IK
wrt_link_id = -1 # robot.get_link_ids('iiwa_link_1')
qIdx = robot.get_q_indices(joint_ids)
# define gains
kp = 50 # 5 if velocity control, 50 if position control
kd = 0 # 2*np.sqrt(kp)
# create sphere to follow
sphere = world.load_visual_sphere(position=np.array([0.5, 0., 1.]),
from itertools import count
from pyrobolearn.simulators import Bullet
from pyrobolearn.worlds import BasicWorld
from pyrobolearn.robots import Epuck
# Create simulator
sim = Bullet()
# create world
world = BasicWorld(sim)
# create robot
robots = []
for _ in range(5):
x, y = np.random.uniform(low=-2, high=2, size=2)
robot = world.load_robot(Epuck, position=(x, y, 0))
robots.append(robot)
# print information about the robot
robots[0].print_info()
# Position control using sliders
# robots[0].add_joint_slider()
# run simulator
for _ in count():
# robots[0].update_joint_slider()
for robot in robots:
robot.drive(5)
world.step(sleep_dt=1. / 240)
from pyrobolearn.tasks import ILTask
# variables
joint_ids = None # None for all the actuated joints, or you can select which joint you want to move; e.g. [0, 1, 2]
num_basis = 20
rate = 30
# Create simulator
sim = Bullet()
# create world
world = BasicWorld(sim)
# load robot in the world
robot = world.load_robot(KukaIIWA)
print("Robot's actuated joint ids: {}".format(robot.joints))
# create state/action
state = ExponentialPhaseState(ticks=rate)
action = JointPositionAction(robot, joint_ids=joint_ids)
print("State: {}".format(state))
print("Action: {}".format(action))
# create environment
env = Env(world, state)
# create DMP policy
policy = BioDiscreteDMPPolicy(action, state, num_basis=num_basis, rate=rate)
# create interface/bridge
print("CoMr: {}".format(CoMr))
if robot.name == 'centauro':
xref_l1f = robot.get_link_world_frame_positions(left_foot1_id) # Desired position for left foot
xref_r1f = robot.get_link_world_frame_positions(right_foot1_id) # Desired position for right foot
xref_l2f = robot.get_link_world_frame_positions(left_foot2_id) # Desired position for left foot
xref_r2f = robot.get_link_world_frame_positions(right_foot2_id) # Desired position for right foot
else:
xref_lf = robot.get_link_world_frame_positions(left_foot_id) # Desired position for left foot
# Qref_lf = robot.get_link_world_frame_orientations(leftFootId)
xref_rf = robot.get_link_world_frame_positions(right_foot_id) # Desired position for right foot
# Qref_rf = robot.get_link_world_frame_orientations(rightFootId)
# Display initial and desired manipulability ellipsoid
robot = world.load_robot(robot)
world.step()
q0 = robot.get_joint_positions()
print("q0: {}".format(q0))
Jcom0 = robot.get_center_of_mass_jacobian(q0)
if not robot.has_fixed_base():
vel_manip = robot.compute_velocity_manipulability_ellipsoid(Jcom0[:, 6:])
else:
vel_manip = robot.compute_velocity_manipulability_ellipsoid(Jcom0)
print("Mv0: {}".format(vel_manip[0:3, 0:3]))
base_pos = robot.get_base_position()
robot.draw_velocity_manipulability_ellipsoid(link_id=-1, JJT=10 * des_vel_manip, color=(0.1, 0.75, 0.1, 0.6))
ellipsoid_id = robot.draw_velocity_manipulability_ellipsoid(link_id=-1, JJT=10 * vel_manip[0:3, 0:3],
color=(0.75, 0.1, 0.1, 0.6))
from pyrobolearn.simulators import Bullet
from pyrobolearn.worlds import BasicWorld
import time
from itertools import count
# create simulator
sim = Bullet()
# sim.configure_debug_visualizer(p.COV_ENABLE_GUI, 0)
# sim.configure_debug_visualizer(p.COV_ENABLE_RENDERING, 0)
# sim.configure_debug_visualizer(p.COV_ENABLE_TINY_RENDERER, 1)
# sim.configure_debug_visualizer(p.COV_ENABLE_WIREFRAME, 1)
# sim.configure_debug_visualizer(p.COV_ENABLE_Y_AXIS_UP, 0)
# sim.configure_debug_visualizer(p.COV_ENABLE_RGB_BUFFER_PREVIEW, 0)
# sim.configure_debug_visualizer(p.COV_ENABLE_DEPTH_BUFFER_PREVIEW, 0)
# sim.configure_debug_visualizer(p.COV_ENABLE_SEGMENTATION_MARK_PREVIEW, 0)
# create World
world = BasicWorld(sim)
# load robot
robot = world.load_robot('baxter', fixed_base=True)
# create bridge/interface
bridge = BridgeMouseKeyboardWorld(world, verbose=True)
for _ in count():
bridge.step(update_interface=True)
# world.step()
time.sleep(1. / 100)
