#!/usr/bin/env python """Load the Minitaur robot. """ from itertools import count from pyrobolearn.simulators import Bullet from pyrobolearn.worlds import BasicWorld from pyrobolearn.robots import Minitaur # Create simulator sim = Bullet() # create world world = BasicWorld(sim) # create robot robot = Minitaur(sim) # print information about the robot robot.print_info() # Position control using sliders # robot.add_joint_slider(robot.left_front_leg) # run simulator for _ in count(): # robot.update_joint_slider() # robot.compute_and_draw_com_position() # robot.compute_and_draw_projected_com_position() world.step(sleep_dt=1./240)
# Create simulator and world sim = Bullet() world = BasicWorld(sim) # load robot if robot_name == 'nao': robot = Nao(sim, fixed_base=False) elif robot_name == 'centauro': robot = Centauro(sim, fixed_base=False) else: raise NotImplementedError("The given robot has not been implemented") # Loop for setting stable initial conditions for i in range(50): world.step(sleep_dt=0.1) # Define velocity manipulability for CoM, proportional gain, and initial configurations if robot.name == 'nao': Km = 100 * np.eye(6) # Proportional gain for Nao for manip. tracking # desired Velocity Manipulability for CoM (Nao) des_manips = np.array([[[1.539e-03, 6.653e-04, 0.833e-04], [6.653e-04, 2.080e-03, -5.843e-05], [0.833e-04, -5.843e-05, 8.601e-04]], [[2.580e-03, 1.653e-03, 4.833e-04], [1.653e-03, 1.539e-03, -5.843e-05], [4.833e-04, -5.843e-05, 9.601e-04]], [[1.580e-03, .653e-03, 4.833e-04], [.653e-03, 1.539e-03, -5.843e-05], [4.833e-04, -5.843e-05, 9.601e-04]],
sphere.position = np.array([ 0.5, r * np.cos(w * t + np.pi / 2), (1. - r) + r * np.sin(w * t + np.pi / 2) ]) # get current end-effector position and velocity in the task/operational space x = robot.get_link_world_positions(link_id) dx = robot.get_link_world_linear_velocities(link_id) # Get joint positions q = robot.get_joint_positions() # Get linear jacobian if robot.has_floating_base(): J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6] else: J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx] # Pseudo-inverse: \hat{J} = J^T (JJ^T + k^2 I)^{-1} Jp = robot.get_damped_least_squares_inverse(J, damping) # evaluate damped-least-squares IK dq = Jp.dot(kp * (sphere.position - x) - kd * dx) # set joint positions q = q[qIdx] + dq * dt robot.set_joint_positions(q, joint_ids=joint_ids) # step in simulation world.step(sleep_dt=dt)
t.join() # stop connection self.connection.close() self.sock.close() # Test if __name__ == "__main__": from pyrobolearn.simulators import Bullet from pyrobolearn.worlds import BasicWorld import time import numpy as np from itertools import count # create simulator sim = Bullet() # create world world = BasicWorld(sim) # create interface interface = OculusInterface(world, port=5111) # run simulation for t in count(): interface.step() # interface.print_state() # step in the simulation world.step() time.sleep(1. / 60)
class DemoRecEnv: def __init__(self, demo_dt, demo_dir): # Initialize XBox controller self.controller = XboxControllerInterface(use_thread=True, verbose=False) # Create simulator self.sim = Bullet() # create world self.world = BasicWorld(self.sim) # Create Robot self.robot = Robot(self.sim, self.world) self.robot.go_home() self.workspace_outer_bound = {'rad': 1.0} self.workspace_inner_bound = {'rad': 0.25, 'height': 0.7} self.env_objects = [] self.demo_dir = demo_dir self.demo_dt = demo_dt def init_task_recorder(self): num_viapts = 2 # int(input("How many via points?")) via_pts = [] for i in range(num_viapts): pt = generate_pt(via_pts, self.workspace_outer_bound, self.workspace_inner_bound) via_pts.append(pt) self.env_objects.append( self.world.load_visual_sphere(pt, radius=0.05, color=(1, 0, 0, 1))) print("Via-pts generated.") print( "Press 'start' to start/stop recording demonstrations,select to end training and Y to reset via pts" ) while True: last_trigger = self.controller.last_updated_button trigger_value = self.controller[last_trigger] if last_trigger == 'menu' and trigger_value == 1: self.start_recording() elif last_trigger == 'view' and trigger_value == 1: print("Ending Training") self.reset_env() break elif last_trigger == 'Y' and trigger_value == 1: for obj in self.env_objects: self.world.sim.remove_body(obj) self.init_task_recorder() return def start_recording(self): time.sleep(1) rec_traj = [] print( "Started Recording. Press 'start' again to stop recording and 'A' to send robot to end" ) sphere = None X_desired = self.robot.robot.sim.get_link_world_positions( body_id=self.robot.robot.id, link_ids=self.robot.ee_id) speed_scale_factor = 0.01 time_ind = 0 save_every = 3 for t in count(): last_trigger = self.controller.last_updated_button trigger_value = self.controller[last_trigger] movement = self.controller[last_trigger] if last_trigger == 'menu' and trigger_value == 1: print("Stopping recording and resetting environment") self.save_traj(rec_traj) self.reset_env() elif last_trigger == 'A' and trigger_value == 1: X_desired = self.robot.end_pos # Update target robot location from controller input elif last_trigger == 'LJ': X_desired[0] = X_desired[0] + round(movement[0], 1) * speed_scale_factor X_desired[1] = X_desired[1] + round(movement[1], 1) * speed_scale_factor elif last_trigger == 'RJ': X_desired[2] = X_desired[2] + round(movement[1], 1) * speed_scale_factor # Record points every 10 iterations if not t % save_every: x = self.robot.robot.sim.get_link_world_positions( body_id=self.robot.robot.id, link_ids=self.robot.ee_id) dx = self.robot.robot.get_link_world_linear_velocities( self.robot.ee_id) rec_traj.append((time_ind * self.demo_dt, ) + tuple(round(i, 3) for i in x) + tuple(round(i, 3) for i in dx)) time_ind = time_ind + 1 # Visualize target EE location if sphere: self.world.sim.remove_body(sphere) sphere = self.world.load_visual_sphere(X_desired, radius=0.01, color=(0, 1, 0, 1)) # Send robot to target location self.robot.send_robot_to(X_desired) self.world.step() def reset_env(self): self.robot.go_home() self.remove_objects() self.init_task_recorder() def remove_objects(self): for obj in self.env_objects: self.world.sim.remove_body(obj) def step_env(self): self.controller.step() self.world.step() def save_traj(self, rec_traj): # Finding the index of last file saved print(rec_traj[:10]) listdir = os.listdir(self.demo_dir) listdir = list(map(int, listdir)) listdir.sort() # print(listdir) ind_last_file = 0 if len(listdir) == 0 else int(listdir[-1]) # print(ind_last_file) del (rec_traj[0]) with open(self.demo_dir + str(ind_last_file + 1), 'w') as f: write = csv.writer(f) write.writerows(rec_traj)
class Environment: def __init__(self, spheres=[[0.09, -0.44, 0.715, 1]], cuboids=None): # Define size of the map self.res = 1. # Initialize XBox controller self.controller = XboxControllerInterface(use_thread=True, verbose=False) # Create simulator self.sim = Bullet() # create world self.world = BasicWorld(self.sim) # create robot self.robot = KukaIIWA(self.sim) # define start/end pt self.start_pt = [-0.75, 0., 0.75] self.end_pt = [0.75, 0., 0.75] # Initialize robot location self.send_robot_to(np.array(self.start_pt), dt=2) # Get via points from user via_pts = self.get_via_pts( ) # [[-0.19, -0.65, 0.77], [0.51, -0.44, 0.58]] self.motion_targets = [self.start_pt] + via_pts + [self.end_pt] def send_robot_to(self, location, dt): start = time.perf_counter() for _ in count(): joint_ids = self.robot.joints link_id = self.robot.get_end_effector_ids(end_effector=0) qIdx = self.robot.get_q_indices(joint_ids) x = self.robot.sim.get_link_world_positions(body_id=self.robot.id, link_ids=link_id) q = self.robot.calculate_inverse_kinematics(link_id, position=location) self.robot.set_joint_positions(q[qIdx], joint_ids) self.world.step() if time.perf_counter() - start >= dt: break def get_via_pts(self): print( "Move green dot to via point. Press 'A' to save. Press 'X' to finish." ) X_desired = [0., 0., 1.3] speed_scale_factor = 0.01 via_pts = [] sphere = None for _ in count(): last_trigger = self.controller.last_updated_button movement = self.controller[last_trigger] if last_trigger == 'LJ': X_desired[0] = X_desired[0] + round(movement[0], 1) * speed_scale_factor X_desired[1] = X_desired[1] + round(movement[1], 1) * speed_scale_factor elif last_trigger == 'RJ': X_desired[2] = X_desired[2] + round(movement[1], 1) * speed_scale_factor elif last_trigger == 'A' and movement == 1: print("Via point added") via_pts.append(X_desired) time.sleep(0.5) elif last_trigger == 'X' and movement == 1: self.world.sim.remove_body(sphere) print("Via point generation complete.") break if sphere: self.world.sim.remove_body(sphere) sphere = self.world.load_visual_sphere(X_desired, radius=0.01, color=(0, 1, 0, 1)) return via_pts