def bin_picking():
# agent = Agent()
# agent.inference.current_type = 2
# agent = AgentShift()
agent = AgentPredict(prediction_model=Loader.get_model('cube-1', 'predict-bi-gen-5'))
agent.grasp_inference.current_type = 2
camera = Camera(camera_suffixes=Config.camera_suffixes)
episode_history = EpisodeHistory()
gripper = Gripper('172.16.0.2', Config.gripper_speed)
robot = Robot('panda_arm', Config.general_dynamics_rel)
saver = Saver(Config.database_url, Config.grasp_database)
current_bin = Config.start_bin
md = MotionData().with_dynamics(1.0)
gripper.stop()
robot.recover_from_errors()
move_to_safety(robot, md)
move_joints_successful = robot.move_joints(Frames.bin_joint_values[current_bin], md)
if not move_joints_successful:
gripper.move(0.07)
robot.recover_from_errors()
move_to_safety(robot, md)
move_joints_successful = robot.move_joints(Frames.bin_joint_values[current_bin], md)
if Config.mode is Mode.Measure and not Config.home_gripper:
logger.warning('Want to measure without homing gripper?')
elif Config.mode is Mode.Measure and Config.home_gripper:
gripper.homing()
move_to_safety(robot, md)
gripper.homing()
overall_start = time.time()
for epoch in Config.epochs:
while episode_history.total() < epoch.number_episodes:
current_episode = Episode()
current_selection_method = epoch.get_selection_method()
if Config.mode in [Mode.Evaluate, Mode.Perform]:
current_selection_method = epoch.get_selection_method_perform(episode_history.failed_grasps_since_last_success_in_bin(current_bin))
start = time.time()
if (not Config.predict_images) or agent.reinfer_next_time:
robot.recover_from_errors()
robot.move_joints(Frames.bin_joint_values[current_bin], md)
b, c = random.choice(Config.overview_image_angles) if Config.lateral_overview_image else 0, 0
camera_frame_overview = Frames.get_camera_frame(current_bin, b=b, c=c)
if not Frames.is_camera_frame_safe(camera_frame_overview):
continue
if Config.mode is Mode.Measure or Config.lateral_overview_image:
robot.move_cartesian(Frames.camera, camera_frame_overview, md)
image_frame = robot.current_pose(Frames.camera)
images = take_images(current_bin, camera, robot, image_frame=image_frame)
if not Frames.is_gripper_frame_safe(robot.current_pose(Frames.gripper)):
logger.info('Image frame not safe!')
robot.recover_from_errors()
continue
input_images = list(filter(lambda i: i.camera in Config.model_input_suffixes, images))
# if episode_history.data:
# agent.successful_grasp_before = episode_history.data[-1].actions[0].reward > 0
action = agent.infer(input_images, current_selection_method)
action.images = {}
action.save = True
if Config.mode is Mode.Measure:
saver.save_image(images, current_episode.id, 'v', action=action)
if Config.mode is not Mode.Perform:
saver.save_action_plan(action, current_episode.id)
logger.info(f'Action: {action} at time {time.time() - overall_start:0.1f}')
action.reward = 0.0
action.collision = False
action.bin = current_bin
if Config.set_zero_reward:
action.safe = -1
if action.type == 'bin_empty':
action.save = False
elif action.type == 'new_image':
action.save = False
agent.reinfer_next_time = True
if action.safe == 0:
logger.warning('Action ignored.')
action.save = False
else:
if Config.mode is Mode.Measure and Config.take_lateral_images and action.save:
md_lateral = MotionData().with_dynamics(1.0)
for b, c in Config.lateral_images_angles:
lateral_frame = Frames.get_camera_frame(current_bin, a=action.pose.a, b=b, c=c, reference_pose=image_frame)
if not Frames.is_camera_frame_safe(lateral_frame) or (b == 0.0 and c == 0.0):
continue
lateral_move_succss = robot.move_cartesian(Frames.camera, lateral_frame, md_lateral) # Remove a for global b, c pose
if lateral_move_succss:
saver.save_image(take_images(current_bin, camera, robot), current_episode.id, f'lateral_b{b:0.3f}_c{c:0.3f}'.replace('.', '_'), action=action)
if action.safe == 1 and action.type not in ['bin_empty', 'new_image']:
action_frame = Frames.get_action_pose(action_pose=action.pose, image_pose=image_frame)
if Config.mode is Mode.Measure and Config.take_direct_images:
robot.move_cartesian(Frames.camera, Affine(z=0.308) * Affine(b=0.0, c=0.0) * action_frame, md)
saver.save_image(take_images(current_bin, camera, robot), current_episode.id, 'direct', action=action)
if action.type == 'grasp':
grasp(robot, gripper, current_episode, current_bin, action, action_frame, image_frame, camera, saver, md)
elif action.type == 'shift':
old_reward_around_action = 0.0
shift(robot, gripper, current_episode, current_bin, action, action_frame, image_frame, camera, saver, md)
new_reward_around_action = 0.0
action.reward = new_reward_around_action - old_reward_around_action
elif action.type == 'place':
last_grasp = episode_history.data[-1].actions[0]
action.grasp_episode_id = episode_history.data[-1].id
place(robot, gripper, current_episode, current_bin, action, action_frame, last_grasp, image_frame, camera, saver, md)
elif action.safe < 0:
logger.info('Pose not found.')
action.collision = True
if Config.take_after_images:
robot.move_cartesian(Frames.camera, image_frame, md)
saver.save_image(take_images(current_bin, camera, robot), current_episode.id, 'after', action=action)
action.execution_time = time.time() - start
logger.info(f'Time for action: {action.execution_time:0.3f} [s]')
if action.save:
current_episode.actions.append(action)
if Config.mode is Mode.Measure:
logger.info(f'Save episode {current_episode.id}.')
saver.save_episode(current_episode)
episode_history.append(current_episode)
logger.info(f'Episodes (reward / done / total): {episode_history.total_reward(action_type="grasp")} / {episode_history.total()} / {sum(e.number_episodes for e in Config.epochs)}')
logger.info(f'Last success: {episode_history.failed_grasps_since_last_success_in_bin(current_bin)} cycles ago.')
# episode_history.save_grasp_rate_prediction_step_evaluation(Config.evaluation_path)
# Change bin
should_change_bin_for_evaluation = (Config.mode is Mode.Evaluate and episode_history.successful_grasps_in_bin(current_bin) == Config.change_bin_at_number_of_success_grasps)
should_change_bin = (Config.mode is not Mode.Evaluate and (episode_history.failed_grasps_since_last_success_in_bin(current_bin) >= Config.change_bin_at_number_of_failed_grasps or action.type == 'bin_empty'))
if should_change_bin_for_evaluation or (Config.change_bins and should_change_bin):
if Config.mode is Mode.Evaluate:
pass
current_bin = Frames.get_next_bin(current_bin)
agent.reinfer_next_time = True
logger.info('Switch to other bin.')
if Config.mode is not Mode.Perform and Config.home_gripper:
gripper.homing()
# Retrain model
if Config.train_model and episode_history.total() > 0 and not episode_history.total() % Config.train_model_every_number_cycles:
logger.warning('Retrain model!')
with open('/tmp/training.txt', 'wb') as out:
p = Popen([sys.executable, str(Config.train_script)], stdout=out)
if not Config.train_async:
p.communicate()
logger.info('Finished cleanly.')
from argparse import ArgumentParser
from cfrankr import Robot, MotionData
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--host',
default='panda_arm',
help='name of the robot')
args = parser.parse_args()
# Setup Robot
general_dynamics_rel = 0.32
robot = Robot(args.host, general_dynamics_rel)
#robot.recover_from_errors()
# Move down
md_home = MotionData().with_dynamics(0.3)
robot.move_joints([
-1.811944, 1.179108, 1.757100, -2.14162, -1.143369, 1.633046, -0.432171
], md_home)
class Experiment:
def __init__(self):
self.camera = Camera(camera_suffixes=Config.camera_suffixes)
self.history = EpisodeHistory()
self.gripper = Gripper('172.16.0.2', Config.gripper_speed,
Config.gripper_force)
self.robot = Robot('panda_arm', Config.general_dynamics_rel)
self.saver = Saver(Config.database_url, Config.collection)
self.current_bin = Config.start_bin
self.md = MotionData().with_dynamics(1.0)
self.overall_start = 0
self.last_after_images: Optional[List[OrthographicImage]] = None
def move_to_release(self,
md: MotionData,
direct=False,
target=None) -> bool:
possible_random_affine = Affine()
if Config.random_pose_before_release:
possible_random_affine = Config.max_random_affine_before_release.get_inner_random(
)
target = target if target else Frames.get_release_frame(
Frames.get_next_bin(self.current_bin)) * possible_random_affine
self.robot.recover_from_errors()
if Config.mode is Mode.Measure:
self.move_to_safety(md)
if Config.release_in_other_bin:
if Config.release_as_fast_as_possible:
waypoints = [
Waypoint(Frames.release_fastest,
Waypoint.ReferenceType.ABSOLUTE)
]
else:
waypoints = [Waypoint(target, Waypoint.ReferenceType.ABSOLUTE)]
if not direct:
waypoints.insert(
0,
Waypoint(Frames.release_midway,
Waypoint.ReferenceType.ABSOLUTE))
return self.robot.move_waypoints_cartesian(Frames.gripper,
waypoints, MotionData())
return self.robot.move_cartesian(
Frames.gripper,
Frames.get_release_frame(self.current_bin) *
possible_random_affine, MotionData())
def move_to_safety(self, md: MotionData) -> bool:
move_up = max(0.0, 0.16 - self.robot.current_pose(Frames.gripper).z)
return self.robot.move_relative_cartesian(Frames.gripper,
Affine(z=move_up), md)
def take_images(self,
image_frame: Affine = None,
current_bin=None) -> List[OrthographicImage]:
current_bin = current_bin if current_bin else self.current_bin
images = self.camera.take_images()
if not image_frame:
image_frame = self.robot.current_pose(Frames.camera)
pose = RobotPose(affine=(image_frame.inverse() *
Frames.get_frame(current_bin)))
for image in images:
image.pose = pose.to_array()
return images
def grasp(self, current_episode: Episode, action_id: int, action: Action,
action_frame: Affine, image_frame: Affine):
md_approach_down = MotionData().with_dynamics(
0.3).with_z_force_condition(7.0)
md_approach_up = MotionData().with_dynamics(
1.0).with_z_force_condition(20.0)
action_approch_affine = Affine(z=Config.approach_distance_from_pose)
action_approach_frame = action_frame * action_approch_affine
try:
process_gripper = Process(target=self.gripper.move,
args=(action.pose.d, ))
process_gripper.start()
self.robot.move_cartesian(Frames.gripper, action_approach_frame,
self.md)
process_gripper.join()
except OSError:
self.gripper.move(0.08)
self.robot.move_cartesian(Frames.gripper, action_approach_frame,
self.md)
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine.inverse(),
md_approach_down)
if md_approach_down.did_break:
self.robot.recover_from_errors()
action.collision = True
self.robot.move_relative_cartesian(Frames.gripper, Affine(z=0.001),
md_approach_up)
action.final_pose = RobotPose(
affine=(image_frame.inverse() *
self.robot.current_pose(Frames.gripper)))
first_grasp_successful = self.gripper.clamp()
if first_grasp_successful:
logger.info('Grasp successful at first.')
self.robot.recover_from_errors()
action_approch_affine = Affine(
z=Config.approach_distance_from_pose)
move_up_success = self.robot.move_relative_cartesian(
Frames.gripper, action_approch_affine, md_approach_up)
if move_up_success and not md_approach_up.did_break:
if Config.mode is Mode.Measure and Config.take_after_images and not Config.release_in_other_bin:
self.robot.move_cartesian(Frames.camera, image_frame,
self.md)
self.last_after_images = self.take_images()
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
if Config.release_during_grasp_action:
move_to_release_success = self.move_to_release(self.md)
if move_to_release_success:
if self.gripper.is_grasping():
action.reward = 1.0
action.final_pose.d = self.gripper.width()
if Config.mode is Mode.Perform:
self.gripper.release(action.final_pose.d +
0.005) # [m]
else:
self.gripper.release(action.pose.d + 0.005) # [m]
self.move_to_safety(md_approach_up)
if Config.mode is Mode.Measure and Config.take_after_images and Config.release_in_other_bin:
self.robot.move_cartesian(Frames.camera,
image_frame, self.md)
self.last_after_images = self.take_images()
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
else:
if Config.mode is not Mode.Perform:
self.move_to_safety(md_approach_up)
if self.gripper.is_grasping():
action.reward = 1.0
action.final_pose.d = self.gripper.width()
if Config.mode is Mode.Measure and Config.take_after_images:
self.robot.move_cartesian(Frames.camera, image_frame,
self.md)
self.last_after_images = self.take_images()
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
else:
self.gripper.release(action.pose.d + 0.002) # [m]
self.robot.recover_from_errors()
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine,
md_approach_up)
move_to_safety_success = self.move_to_safety(md_approach_up)
if not move_to_safety_success:
self.robot.recover_from_errors()
self.robot.recover_from_errors()
self.move_to_safety(md_approach_up)
self.gripper.move(self.gripper.max_width)
self.move_to_safety(md_approach_up)
if Config.mode is Mode.Measure and Config.take_after_images:
self.robot.move_cartesian(Frames.camera, image_frame,
self.md)
self.last_after_images = self.take_images()
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
else:
logger.info('Grasp not successful.')
self.gripper.release(self.gripper.width() + 0.002) # [m]
self.robot.recover_from_errors()
move_up_successful = self.robot.move_relative_cartesian(
Frames.gripper, action_approch_affine, md_approach_up)
if md_approach_up.did_break or not move_up_successful:
self.gripper.release(action.pose.d) # [m]
self.robot.recover_from_errors()
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine,
md_approach_up)
self.move_to_safety(md_approach_up)
if Config.mode is Mode.Measure and Config.take_after_images:
self.robot.move_cartesian(Frames.camera, image_frame, self.md)
self.last_after_images = self.take_images()
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
def shift(self, current_episode: Episode, action_id: int, action: Action,
action_frame: Affine, image_frame: Affine):
md_approach_down = MotionData().with_dynamics(
0.15).with_z_force_condition(6.0)
md_approach_up = MotionData().with_dynamics(
0.6).with_z_force_condition(20.0)
md_shift = MotionData().with_dynamics(0.1).with_xy_force_condition(
10.0)
action_approch_affine = Affine(z=Config.approach_distance_from_pose)
action_approach_frame = action_approch_affine * action_frame
try:
process_gripper = Process(target=self.gripper.move,
args=(action.pose.d, ))
process_gripper.start()
self.robot.move_cartesian(Frames.gripper, action_approach_frame,
self.md)
process_gripper.join()
except OSError:
self.gripper.move(0.08)
self.robot.move_cartesian(Frames.gripper, action_approach_frame,
self.md)
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine.inverse(),
md_approach_down)
if md_approach_down.did_break:
self.robot.recover_from_errors()
action.collision = True
self.robot.move_relative_cartesian(Frames.gripper, Affine(z=0.001),
md_approach_up)
self.robot.move_relative_cartesian(
Frames.gripper,
Affine(x=action.shift_motion[0], y=action.shift_motion[1]),
md_shift)
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine,
md_approach_up)
# Reward is set outside of this function, due to dependency on agent
def place(self,
current_episode: Episode,
action_id: int,
action: Action,
action_frame: Affine,
image_frame: Affine,
place_bin=None):
place_bin = place_bin if place_bin else self.current_bin
self.move_to_safety(self.md)
md_approach_down = MotionData().with_dynamics(
0.22).with_z_force_condition(7.0)
md_approach_up = MotionData().with_dynamics(
1.0).with_z_force_condition(20.0)
action_approch_affine = Affine(z=Config.approach_distance_from_pose)
action_approach_frame = action_frame * action_approch_affine
if Config.release_in_other_bin:
self.move_to_release(self.md, target=action_approach_frame)
else:
self.robot.move_cartesian(Frames.gripper, action_approach_frame,
self.md)
self.robot.move_relative_cartesian(Frames.gripper,
action_approch_affine.inverse(),
md_approach_down)
if md_approach_down.did_break:
self.robot.recover_from_errors()
action.collision = True
self.robot.move_relative_cartesian(Frames.gripper, Affine(z=0.001),
md_approach_up)
action.final_pose = RobotPose(
affine=(image_frame.inverse() *
self.robot.current_pose(Frames.gripper)))
action.pose.d = self.gripper.width()
self.gripper.release(action.pose.d + 0.01) # [m]
if Config.mode is not Mode.Perform:
self.move_to_safety(md_approach_up)
if Config.mode is Mode.Measure and Config.take_after_images:
self.robot.move_cartesian(Frames.camera, image_frame, self.md)
self.last_after_images = self.take_images(current_bin=place_bin)
self.saver.save_image(self.last_after_images,
current_episode.id,
action_id,
'after',
action=action)
def init(self) -> None:
self.gripper.stop()
self.robot.recover_from_errors()
self.move_to_safety(self.md)
move_joints_successful = self.robot.move_joints(
Frames.bin_joint_values[self.current_bin], self.md)
if not move_joints_successful:
self.gripper.move(0.07)
self.robot.recover_from_errors()
self.move_to_safety(self.md)
move_joints_successful = self.robot.move_joints(
Frames.bin_joint_values[self.current_bin], self.md)
if Config.mode is Mode.Measure and not Config.home_gripper:
logger.warning('Want to measure without homing gripper?')
elif Config.home_gripper:
self.gripper.homing()
self.move_to_safety(self.md)
self.overall_start = time.time()
def retrain_model(self) -> None:
with open('/tmp/training.txt', 'wb') as out:
p = Popen([sys.executable, str(Config.train_script)], stdout=out)
if not Config.train_async:
p.communicate()
def get_current_selection_method(self, epoch) -> SelectionMethod:
if Config.mode in [Mode.Evaluate, Mode.Perform]:
return epoch.get_selection_method_perform(
self.history.failed_grasps_since_last_success_in_bin(
self.current_bin))
return epoch.get_selection_method()
def get_input_images(self, images):
return list(
filter(lambda i: i.camera in Config.model_input_suffixes, images))