def load_world():
# TODO: store internal world info here to be reloaded
set_default_camera()
draw_global_system()
with HideOutput():
#add_data_path()
robot = load_model(DRAKE_IIWA_URDF, fixed_base=True) # DRAKE_IIWA_URDF | KUKA_IIWA_URDF
floor = load_model('models/short_floor.urdf')
sink = load_model(SINK_URDF, pose=Pose(Point(x=-0.5)))
stove = load_model(STOVE_URDF, pose=Pose(Point(x=+0.5)))
celery = load_model(BLOCK_URDF, fixed_base=False)
radish = load_model(SMALL_BLOCK_URDF, fixed_base=False)
#cup = load_model('models/dinnerware/cup/cup_small.urdf',
# Pose(Point(x=+0.5, y=+0.5, z=0.5)), fixed_base=False)
draw_pose(Pose(), parent=robot, parent_link=get_tool_link(robot)) # TODO: not working
# dump_body(robot)
# wait_for_user()
body_names = {
sink: 'sink',
stove: 'stove',
celery: 'celery',
radish: 'radish',
}
movable_bodies = [celery, radish]
set_pose(celery, Pose(Point(y=0.5, z=stable_z(celery, floor))))
set_pose(radish, Pose(Point(y=-0.5, z=stable_z(radish, floor))))
return robot, body_names, movable_bodies
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning')
args = parser.parse_args()
print(args)
connect(use_gui=True)
with LockRenderer():
draw_pose(unit_pose(), length=1)
floor = create_floor()
with HideOutput():
robot = load_pybullet(get_model_path(ROOMBA_URDF), fixed_base=True, scale=2.0)
for link in get_all_links(robot):
set_color(robot, link=link, color=ORANGE)
robot_z = stable_z(robot, floor)
set_point(robot, Point(z=robot_z))
#set_base_conf(robot, rover_confs[i])
data_path = add_data_path()
shelf, = load_model(os.path.join(data_path, KIVA_SHELF_SDF), fixed_base=True) # TODO: returns a tuple
dump_body(shelf)
#draw_aabb(get_aabb(shelf))
wait_for_user()
disconnect()
def load_world():
# TODO: store internal world info here to be reloaded
with HideOutput():
robot = load_model(DRAKE_IIWA_URDF)
#add_data_path()
#robot = load_pybullet(KUKA_IIWA_URDF)
floor = load_model('models/short_floor.urdf')
sink = load_model(SINK_URDF, pose=Pose(Point(x=-0.5)))
stove = load_model(STOVE_URDF, pose=Pose(Point(x=+0.5)))
celery = load_model(BLOCK_URDF, fixed_base=False)
radish = load_model(SMALL_BLOCK_URDF, fixed_base=False)
#cup = load_model('models/dinnerware/cup/cup_small.urdf',
# Pose(Point(x=+0.5, y=+0.5, z=0.5)), fixed_base=False)
body_names = {
sink: 'sink',
stove: 'stove',
celery: 'celery',
radish: 'radish',
}
movable_bodies = [celery, radish]
set_pose(celery, Pose(Point(y=0.5, z=stable_z(celery, floor))))
set_pose(radish, Pose(Point(y=-0.5, z=stable_z(radish, floor))))
set_default_camera()
return robot, body_names, movable_bodies
def load_world():
root_directory = os.path.dirname(os.path.abspath(__file__))
with HideOutput():
floor = load_model('models/short_floor.urdf')
robot = load_pybullet(os.path.join(root_directory, KUKA_PATH), fixed_base=True)
set_point(floor, Point(z=-0.01))
return floor, robot
def get_kitchen_task(arm='left', grasp_type='top'):
with HideOutput():
pr2 = create_pr2(use_drake=USE_DRAKE_PR2)
set_arm_conf(pr2, arm, get_carry_conf(arm, grasp_type))
open_arm(pr2, arm)
other_arm = get_other_arm(arm)
set_arm_conf(pr2, other_arm, arm_conf(other_arm, REST_LEFT_ARM))
close_arm(pr2, other_arm)
table, cabbage, sink, stove = create_kitchen()
floor = get_bodies()[1]
class_from_body = {
table: 'table',
cabbage: 'cabbage',
sink: 'sink',
stove: 'stove',
} # TODO: use for debug
movable = [cabbage]
surfaces = [table, sink, stove]
rooms = [floor]
return BeliefTask(robot=pr2, arms=[arm], grasp_types=[grasp_type],
class_from_body=class_from_body,
movable=movable, surfaces=surfaces, rooms=rooms,
#goal_localized=[cabbage],
#goal_registered=[cabbage],
#goal_holding=[(arm, cabbage)],
#goal_on=[(cabbage, table)],
goal_on=[(cabbage, sink)],
)
def plan_commands(state,
viewer=False,
teleport=False,
profile=False,
verbose=True):
# TODO: could make indices into set of bodies to ensure the same...
# TODO: populate the bodies here from state and not the real world
sim_world = connect(use_gui=viewer)
#clone_world(client=sim_world)
task = state.task
robot_conf = get_configuration(task.robot)
robot_pose = get_pose(task.robot)
with ClientSaver(sim_world):
with HideOutput():
robot = create_pr2(use_drake=USE_DRAKE_PR2)
set_pose(robot, robot_pose)
set_configuration(robot, robot_conf)
mapping = clone_world(client=sim_world, exclude=[task.robot])
assert all(i1 == i2 for i1, i2 in mapping.items())
set_client(sim_world)
saved_world = WorldSaver() # StateSaver()
pddlstream_problem = pddlstream_from_state(state, teleport=teleport)
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', sorted(init, key=lambda f: f[0]))
if verbose:
print('Goal:', goal)
print('Streams:', stream_map.keys())
stream_info = {
'test-vis-base': StreamInfo(eager=True, p_success=0),
'test-reg-base': StreamInfo(eager=True, p_success=0),
}
hierarchy = [
ABSTRIPSLayer(pos_pre=['AtBConf']),
]
pr = cProfile.Profile()
pr.enable()
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
hierarchy=hierarchy,
debug=False,
success_cost=MAX_COST,
verbose=verbose)
plan, cost, evaluations = solution
if MAX_COST <= cost:
plan = None
print_solution(solution)
print('Finite cost:', cost < MAX_COST)
commands = post_process(state, plan)
pr.disable()
if profile:
pstats.Stats(pr).sort_stats('cumtime').print_stats(10)
saved_world.restore()
disconnect()
return commands
def main():
parser = argparse.ArgumentParser() # Automatically includes help
parser.add_argument('-viewer', action='store_true', help='enable viewer.')
args = parser.parse_args()
connect(use_gui=True)
#ycb_path = os.path.join(DRAKE_PATH, DRAKE_YCB)
#ycb_path = os.path.join(YCB_PATH, YCB_TEMPLATE.format('003_cracker_box'))
#print(ycb_path)
#load_pybullet(ycb_path)
with LockRenderer():
draw_pose(unit_pose(), length=1, width=1)
floor = create_floor()
set_point(floor, Point(z=-EPSILON))
table = create_table(width=TABLE_WIDTH, length=TABLE_WIDTH/2, height=TABLE_WIDTH/2, top_color=TAN, cylinder=False)
#set_euler(table, Euler(yaw=np.pi/2))
with HideOutput(False):
# data_path = add_data_path()
# robot_path = os.path.join(data_path, WSG_GRIPPER)
robot_path = get_model_path(WSG_50_URDF) # WSG_50_URDF | PANDA_HAND_URDF
#robot_path = get_file_path(__file__, 'mit_arch_suction_gripper/mit_arch_suction_gripper.urdf')
robot = load_pybullet(robot_path, fixed_base=True)
#dump_body(robot)
#robot = create_cylinder(radius=0.5*BLOCK_SIDE, height=4*BLOCK_SIDE) # vacuum gripper
block1 = create_box(w=BLOCK_SIDE, l=BLOCK_SIDE, h=BLOCK_SIDE, color=RED)
block_z = stable_z(block1, table)
set_point(block1, Point(z=block_z))
block2 = create_box(w=BLOCK_SIDE, l=BLOCK_SIDE, h=BLOCK_SIDE, color=GREEN)
set_point(block2, Point(x=+0.25, z=block_z))
block3 = create_box(w=BLOCK_SIDE, l=BLOCK_SIDE, h=BLOCK_SIDE, color=BLUE)
set_point(block3, Point(x=-0.15, z=block_z))
blocks = [block1, block2, block3]
add_line(Point(x=-TABLE_WIDTH/2, z=block_z - BLOCK_SIDE/2 + EPSILON),
Point(x=+TABLE_WIDTH/2, z=block_z - BLOCK_SIDE/2 + EPSILON), color=RED)
set_camera_pose(camera_point=Point(y=-1, z=block_z+1), target_point=Point(z=block_z))
wait_for_user()
block_pose = get_pose(block1)
open_gripper(robot)
tool_link = link_from_name(robot, 'tool_link')
base_from_tool = get_relative_pose(robot, tool_link)
#draw_pose(unit_pose(), parent=robot, parent_link=tool_link)
y_grasp, x_grasp = get_top_grasps(block1, tool_pose=unit_pose(), grasp_length=0.03, under=False)
grasp = y_grasp # fingers won't collide
gripper_pose = multiply(block_pose, invert(grasp))
set_pose(robot, multiply(gripper_pose, invert(base_from_tool)))
wait_for_user('Finish?')
disconnect()
def load_world():
# TODO: store internal world info here to be reloaded
with HideOutput():
robot = load_model(DRAKE_IIWA_URDF)
# robot = load_model(KUKA_IIWA_URDF)
floor = load_model('models/short_floor.urdf')
sink = load_model(SINK_URDF, pose=Pose(Point(x=-0.5)))
stove = load_model(STOVE_URDF, pose=Pose(Point(x=+0.5)))
block = load_model(BLOCK_URDF, fixed_base=False)
block1 = load_model(BLOCK_URDF, fixed_base=False)
block2 = load_model(BLOCK_URDF, fixed_base=False)
block3 = load_model(BLOCK_URDF, fixed_base=False)
block4 = load_model(BLOCK_URDF, fixed_base=False)
block5 = load_model(BLOCK_URDF, fixed_base=False)
block6 = load_model(BLOCK_URDF, fixed_base=False)
block7 = load_model(BLOCK_URDF, fixed_base=False)
block8 = load_model(BLOCK_URDF, fixed_base=False)
cup = load_model(
'models/cup.urdf', #'models/dinnerware/cup/cup_small.urdf'
fixed_base=False)
body_names = {
sink: 'sink',
stove: 'stove',
block: 'celery',
cup: 'cup',
}
movable_bodies = [
block, cup, block1, block2, block3, block4, block5, block6, block7,
block8
]
set_pose(block, Pose(Point(x=0.1, y=0.5, z=stable_z(block, floor))))
set_pose(block1, Pose(Point(x=-0.1, y=0.5, z=stable_z(block1, floor))))
set_pose(block2, Pose(Point(y=0.35, z=stable_z(block2, floor))))
set_pose(block3, Pose(Point(y=0.65, z=stable_z(block3, floor))))
set_pose(block4, Pose(Point(x=0.1, y=0.65, z=stable_z(block4, floor))))
set_pose(block5, Pose(Point(x=-0.1, y=0.65, z=stable_z(block5, floor))))
set_pose(block6, Pose(Point(x=0.1, y=0.35, z=stable_z(block6, floor))))
set_pose(block7, Pose(Point(x=-0.1, y=0.35, z=stable_z(block7, floor))))
set_pose(block8, Pose(Point(x=0, y=0.5, z=0.45)))
set_pose(cup, Pose(Point(y=0.5, z=stable_z(cup, floor))))
set_default_camera()
return robot, body_names, movable_bodies
def plan_commands(state, teleport=False, profile=False, verbose=True):
# TODO: could make indices into set of bodies to ensure the same...
# TODO: populate the bodies here from state
task = state.task
robot_conf = get_configuration(task.robot)
robot_pose = get_pose(task.robot)
sim_world = connect(use_gui=False)
with ClientSaver(sim_world):
with HideOutput():
robot = create_pr2(use_drake=USE_DRAKE_PR2)
#robot = load_model(DRAKE_PR2_URDF, fixed_base=True)
set_pose(robot, robot_pose)
set_configuration(robot, robot_conf)
clone_world(client=sim_world, exclude=[task.robot])
set_client(sim_world)
saved_world = WorldSaver() # StateSaver()
pddlstream_problem = pddlstream_from_state(state, teleport=teleport)
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', sorted(init, key=lambda f: f[0]))
if verbose:
print('Goal:', goal)
print('Streams:', stream_map.keys())
stream_info = {
'test-vis-base': StreamInfo(eager=True, p_success=0),
'test-reg-base': StreamInfo(eager=True, p_success=0),
}
pr = cProfile.Profile()
pr.enable()
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
max_cost=MAX_COST,
verbose=verbose)
pr.disable()
plan, cost, evaluations = solution
if MAX_COST <= cost:
plan = None
print_solution(solution)
print('Finite cost:', cost < MAX_COST)
print('Real cost:', float(cost) / SCALE_COST)
if profile:
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
saved_world.restore()
commands = post_process(state, plan)
disconnect()
return commands
def problem_fn(n_rovers=1, collisions=True):
base_extent = 2.5
base_limits = (-base_extent / 2. * np.ones(2), base_extent / 2. * np.ones(2))
mound_height = 0.1
floor = create_box(base_extent, base_extent, 0.001, color=TAN) # TODO: two rooms
set_point(floor, Point(z=-0.001 / 2.))
wall1 = create_box(base_extent + mound_height, mound_height, mound_height, color=GREY)
set_point(wall1, Point(y=base_extent / 2., z=mound_height / 2.))
wall2 = create_box(base_extent + mound_height, mound_height, mound_height, color=GREY)
set_point(wall2, Point(y=-base_extent / 2., z=mound_height / 2.))
wall3 = create_box(mound_height, base_extent + mound_height, mound_height, color=GREY)
set_point(wall3, Point(x=base_extent / 2., z=mound_height / 2.))
wall4 = create_box(mound_height, base_extent + mound_height, mound_height, color=GREY)
set_point(wall4, Point(x=-base_extent / 2., z=mound_height / 2.))
wall5 = create_box(mound_height, (base_extent + mound_height)/ 2., mound_height, color=GREY)
set_point(wall5, Point(y=base_extent / 4., z=mound_height / 2.))
rover_confs = [(+1, 0, np.pi), (-1, 0, 0)]
assert n_rovers <= len(rover_confs)
robots = []
for i in range(n_rovers):
with HideOutput():
rover = load_model(TURTLEBOT_URDF)
robot_z = stable_z(rover, floor)
set_point(rover, Point(z=robot_z))
set_base_conf(rover, rover_confs[i])
robots.append(rover)
goal_confs = {robots[0]: rover_confs[-1]}
#goal_confs = {}
# TODO: make the objects smaller
cylinder_radius = 0.25
body1 = create_cylinder(cylinder_radius, mound_height, color=RED)
set_point(body1, Point(y=-base_extent / 4., z=mound_height / 2.))
body2 = create_cylinder(cylinder_radius, mound_height, color=BLUE)
set_point(body2, Point(x=base_extent / 4., y=3*base_extent / 8., z=mound_height / 2.))
movable = [body1, body2]
#goal_holding = {robots[0]: body1}
goal_holding = {}
return NAMOProblem(robots, base_limits, movable, collisions=collisions,
goal_holding=goal_holding, goal_confs=goal_confs)
def problem_fn(n_robots=2, collisions=True):
base_extent = 2.0
base_limits = (-base_extent / 2. * np.ones(2),
base_extent / 2. * np.ones(2))
mound_height = 0.1
floor, walls = create_environment(base_extent, mound_height)
width = base_extent / 2. - 4 * mound_height
wall5 = create_box(mound_height, width, mound_height, color=GREY)
set_point(wall5,
Point(y=-(base_extent / 2 - width / 2.), z=mound_height / 2.))
wall6 = create_box(mound_height, width, mound_height, color=GREY)
set_point(wall6,
Point(y=+(base_extent / 2 - width / 2.), z=mound_height / 2.))
distance = 0.5
#initial_confs = [(-distance, -distance, 0),
# (-distance, +distance, 0)]
initial_confs = [(-distance, -distance, 0), (+distance, +distance, 0)]
assert n_robots <= len(initial_confs)
body_from_name = {}
#robots = ['green']
robots = ['green', 'blue']
with LockRenderer():
for i, name in enumerate(robots):
with HideOutput():
body = load_model(
TURTLEBOT_URDF) # TURTLEBOT_URDF | ROOMBA_URDF
body_from_name[name] = body
robot_z = stable_z(body, floor)
set_point(body, Point(z=robot_z))
set_base_conf(body, initial_confs[i])
set_body_color(body, COLOR_FROM_NAME[name])
goals = [(+distance, -distance, 0), (+distance, +distance, 0)]
#goals = goals[::-1]
goals = initial_confs[::-1]
goal_confs = dict(zip(robots, goals))
return NAMOProblem(body_from_name,
robots,
base_limits,
collisions=collisions,
goal_confs=goal_confs)
def load_world():
# TODO: store internal world info here to be reloaded
with HideOutput():
robot = load_model(DRAKE_IIWA_URDF)
# robot = load_model(KUKA_IIWA_URDF)
floor = load_model('models/short_floor.urdf')
sink = load_model(SINK_URDF, pose=Pose(Point(x=-0.5)))
stove = load_model(STOVE_URDF, pose=Pose(Point(x=+0.5)))
block = load_model(BLOCK_URDF, fixed_base=False)
#cup = load_model('models/dinnerware/cup/cup_small.urdf',
# Pose(Point(x=+0.5, y=+0.5, z=0.5)), fixed_base=False)
body_names = {
sink: 'sink',
stove: 'stove',
block: 'celery',
}
movable_bodies = [block]
set_pose(block, Pose(Point(y=0.5, z=stable_z(block, floor))))
set_default_camera()
return robot, body_names, movable_bodies
def rovers1(n_rovers=2,
n_objectives=4,
n_rocks=3,
n_soil=3,
n_stores=1,
n_obstacles=8):
base_extent = 5.0
base_limits = (-base_extent / 2. * np.ones(2),
base_extent / 2. * np.ones(2))
mount_width = 0.5
mound_height = 0.1
floor = create_box(base_extent, base_extent, 0.001,
color=TAN) # TODO: two rooms
set_point(floor, Point(z=-0.001 / 2.))
wall1 = create_box(base_extent + mound_height,
mound_height,
mound_height,
color=GREY)
set_point(wall1, Point(y=base_extent / 2., z=mound_height / 2.))
wall2 = create_box(base_extent + mound_height,
mound_height,
mound_height,
color=GREY)
set_point(wall2, Point(y=-base_extent / 2., z=mound_height / 2.))
wall3 = create_box(mound_height,
base_extent + mound_height,
mound_height,
color=GREY)
set_point(wall3, Point(x=base_extent / 2., z=mound_height / 2.))
wall4 = create_box(mound_height,
base_extent + mound_height,
mound_height,
color=GREY)
set_point(wall4, Point(x=-base_extent / 2., z=mound_height / 2.))
# TODO: can add obstacles along the wall
wall = create_box(mound_height, base_extent, mound_height,
color=GREY) # TODO: two rooms
set_point(wall, Point(z=mound_height / 2.))
add_data_path()
with HideOutput():
lander = load_pybullet(HUSKY_URDF, scale=1)
lander_z = stable_z(lander, floor)
set_point(lander, Point(-1.9, -2, lander_z))
mound1 = create_box(mount_width, mount_width, mound_height, color=GREY)
set_point(mound1, [+2, 2, mound_height / 2.])
mound2 = create_box(mount_width, mount_width, mound_height, color=GREY)
set_point(mound2, [-2, 2, mound_height / 2.])
mound3 = create_box(mount_width, mount_width, mound_height, color=GREY)
set_point(mound3, [+0.5, 2, mound_height / 2.])
mound4 = create_box(mount_width, mount_width, mound_height, color=GREY)
set_point(mound4, [-0.5, 2, mound_height / 2.])
mounds = [mound1, mound2, mound3, mound4]
random.shuffle(mounds)
body_types = []
initial_surfaces = OrderedDict()
min_distances = {}
for _ in range(n_obstacles):
body = create_box(mound_height,
mound_height,
4 * mound_height,
color=GREY)
initial_surfaces[body] = floor
rover_confs = [(+1, -1.75, np.pi), (-1, -1.75, 0)]
assert n_rovers <= len(rover_confs)
landers = [lander]
stores = ['store{}'.format(i) for i in range(n_stores)]
rovers = []
for i in range(n_rovers):
# camera_rgb_optical_frame
with HideOutput():
rover = load_model(TURTLEBOT_URDF)
robot_z = stable_z(rover, floor)
set_point(rover, Point(z=robot_z))
#handles = draw_aabb(get_aabb(rover)) # Includes the origin
#print(get_center_extent(rover))
#wait_for_user()
set_base_conf(rover, rover_confs[i])
rovers.append(rover)
#dump_body(rover)
#draw_pose(get_link_pose(rover, link_from_name(rover, KINECT_FRAME)))
obj_width = 0.07
obj_height = 0.2
objectives = []
for i in range(n_objectives):
body = create_box(obj_width, obj_width, obj_height, color=BLUE)
objectives.append(body)
#initial_surfaces[body] = random.choice(mounds)
initial_surfaces[body] = mounds[i]
min_distances.update({r: 0.05 for r in objectives})
# TODO: it is moving to intermediate locations attempting to reach the rocks
rocks = []
for _ in range(n_rocks):
body = create_box(0.075, 0.075, 0.01, color=BLACK)
rocks.append(body)
body_types.append((body, 'stone'))
for _ in range(n_soil):
body = create_box(0.1, 0.1, 0.005, color=BROWN)
rocks.append(body)
body_types.append((body, 'soil'))
soils = [] # Treating soil as rocks for simplicity
initial_surfaces.update({r: floor for r in rocks})
min_distances.update({r: 0.2 for r in rocks})
sample_placements(initial_surfaces, min_distances=min_distances)
return RoversProblem(rovers, landers, objectives, rocks, soils, stores,
base_limits, body_types)
def main(display=True, teleport=False):
parser = argparse.ArgumentParser()
parser.add_argument('-problem',
default='rovers1',
help='The name of the problem to solve')
parser.add_argument('-algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-t',
'--max_time',
default=120,
type=int,
help='The max time')
parser.add_argument('-unit', action='store_true', help='Uses unit costs')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='enable the viewer while planning')
args = parser.parse_args()
print(args)
problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_fn_from_name:
raise ValueError(args.problem)
problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn()
saver = WorldSaver()
draw_base_limits(problem.limits, color=(1, 0, 0))
pddlstream_problem = pddlstream_from_problem(problem,
collisions=not args.cfree,
teleport=teleport)
stream_info = {
'test-cfree-ray-conf': StreamInfo(negate=True),
'test-reachable': StreamInfo(p_success=1e-1),
'obj-inv-visible': StreamInfo(),
'com-inv-visible': StreamInfo(),
'sample-above': StreamInfo(),
'sample-motion': StreamInfo(overhead=10),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
#print('Streams:', stream_map.keys())
success_cost = 0 if args.optimal else INF
planner = 'ff-wastar3'
search_sample_ratio = 2
max_planner_time = 10
# TODO: need to accelerate samples here because of the failed test reachable
pr = cProfile.Profile()
pr.enable()
with LockRenderer(False):
if args.algorithm == 'focused':
# TODO: option to only consider costs during local optimization
solution = solve_focused(
pddlstream_problem,
stream_info=stream_info,
planner=planner,
max_planner_time=max_planner_time,
debug=False,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True,
unit_efforts=True,
effort_weight=1,
#bind=True, max_skeletons=None,
search_sample_ratio=search_sample_ratio)
elif args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
planner=planner,
max_planner_time=max_planner_time,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime
if plan is None:
return
if (not display) or (plan is None):
disconnect()
return
# Maybe openrave didn't actually sample any joints...
# http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/
with LockRenderer():
commands = post_process(problem, plan, teleport=teleport)
saver.restore() # Assumes bodies are ordered the same way
if not args.viewer:
disconnect()
connect(use_gui=True)
with LockRenderer():
with HideOutput():
problem_fn() # TODO: way of doing this without reloading?
saver.restore() # Assumes bodies are ordered the same way
if args.simulate:
control_commands(commands)
else:
time_step = None if teleport else 0.01
apply_commands(BeliefState(problem), commands, time_step)
wait_for_user()
disconnect()
def main():
parser = create_parser()
parser.add_argument('-problem',
default='rovers1',
help='The name of the problem to solve')
parser.add_argument('-cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-t',
'--max_time',
default=120,
type=int,
help='The max time')
parser.add_argument('-enable',
action='store_true',
help='Enables rendering during planning')
parser.add_argument('-teleport',
action='store_true',
help='Teleports between configurations')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_fn_from_name:
raise ValueError(args.problem)
problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
rovers_problem = problem_fn()
saver = WorldSaver()
draw_base_limits(rovers_problem.limits, color=RED)
pddlstream_problem = pddlstream_from_problem(rovers_problem,
collisions=not args.cfree,
teleport=args.teleport,
holonomic=False,
reversible=True,
use_aabb=True)
stream_info = {
'test-cfree-ray-conf': StreamInfo(),
'test-reachable': StreamInfo(p_success=1e-1),
'obj-inv-visible': StreamInfo(),
'com-inv-visible': StreamInfo(),
'sample-above': StreamInfo(),
'sample-motion': StreamInfo(overhead=10),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
#print('Streams:', stream_map.keys())
success_cost = 0 if args.optimal else INF
planner = 'ff-wastar3'
search_sample_ratio = 2
max_planner_time = 10
# TODO: need to accelerate samples here because of the failed test reachable
with Profiler(field='tottime', num=25):
with LockRenderer(lock=not args.enable):
# TODO: option to only consider costs during local optimization
solution = solve(pddlstream_problem,
algorithm=args.algorithm,
stream_info=stream_info,
planner=planner,
max_planner_time=max_planner_time,
debug=False,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True,
unit_efforts=True,
effort_weight=1,
search_sample_ratio=search_sample_ratio)
for body in get_bodies():
if body not in saver.bodies:
remove_body(body)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
# Maybe OpenRAVE didn't actually sample any joints...
# http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/
with LockRenderer():
commands = post_process(rovers_problem, plan)
saver.restore()
wait_for_user('Begin?')
if args.simulate:
control_commands(commands)
else:
time_step = None if args.teleport else 0.01
apply_commands(BeliefState(rovers_problem), commands, time_step)
wait_for_user('Finish?')
disconnect()
def main(teleport=False):
#parser = create_parser()
parser = argparse.ArgumentParser()
parser.add_argument('-algorithm', default='incremental', help='Specifies the algorithm')
parser.add_argument('-cfree', action='store_true', help='Disables collisions')
parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-t', '--max_time', default=5*60, type=int, help='The max time')
parser.add_argument('-enable', action='store_true', help='Enables rendering during planning')
parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
connect(use_gui=args.viewer)
with HideOutput():
namo_problem = problem_fn(collisions=not args.cfree)
saver = WorldSaver()
draw_base_limits(namo_problem.limits, color=RED)
pddlstream_problem, edges = pddlstream_from_problem(namo_problem, teleport=teleport)
_, constant_map, _, stream_map, init, goal = pddlstream_problem
print('Constants:', constant_map)
print('Init:', init)
print('Goal:', goal)
stream_info = {
'compute-motion': StreamInfo(eager=True, p_success=0),
'ConfConfCollision': PredicateInfo(p_success=1, overhead=0.1),
'TrajConfCollision': PredicateInfo(p_success=1, overhead=1),
'TrajTrajCollision': PredicateInfo(p_success=1, overhead=10),
'TrajDistance': FunctionInfo(eager=True), # Need to eagerly evaluate otherwise 0 duration (failure)
}
success_cost = 0 if args.optimal else INF
max_planner_time = 10
with Profiler(field='tottime', num=25): # cumtime | tottime
with LockRenderer(lock=not args.enable):
# TODO: solution = solve_incremental(pddlstream_problem
if args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
max_planner_time=max_planner_time,
success_cost=success_cost, max_time=args.max_time,
start_complexity=INF,
verbose=True, debug=True)
elif args.algorithm == 'focused':
solution = solve_focused(pddlstream_problem, stream_info=stream_info,
max_planner_time=max_planner_time,
success_cost=success_cost, max_time=args.max_time,
max_skeletons=None, bind=True, max_failures=INF,
verbose=True, debug=True)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
saver.restore()
draw_edges(edges)
state = BeliefState(namo_problem)
wait_for_user('Begin?')
#time_step = None if teleport else 0.01
#with VideoSaver('video.mp4'):
display_plan(namo_problem, state, plan)
wait_for_user('Finish?')
disconnect()
def load_pick_and_place(extrusion_name, scale=MILLIMETER, max_bricks=6):
assert extrusion_name == 'choreo_brick_demo'
root_directory = os.path.dirname(os.path.abspath(__file__))
bricks_directory = os.path.join(root_directory, PICKNPLACE_DIRECTORY,
'bricks')
print('Name: {}'.format(extrusion_name))
with open(
os.path.join(bricks_directory,
PICKNPLACE_FILENAMES[extrusion_name]), 'r') as f:
json_data = json.loads(f.read())
kuka_urdf = '../models/framefab_kr6_r900_support/urdf/kr6_r900_mit_suction_gripper.urdf'
obj_directory = os.path.join(bricks_directory, 'meshes', 'collision')
with HideOutput():
#world = load_pybullet(os.path.join(bricks_directory, 'urdf', 'brick_demo.urdf'))
robot = load_pybullet(os.path.join(root_directory, kuka_urdf),
fixed_base=True)
#set_point(robot, (0.14, 0, 0))
#dump_body(robot)
pick_base_point = parse_point(json_data['pick_base_center_point'])
draw_pose((pick_base_point, unit_quat()))
place_base_point = parse_point(json_data['place_base_center_point'])
draw_pose((place_base_point, unit_quat()))
# workspace_geo_place_support_object_11 = pick_left_over_bricks_11
obstacle_from_name = {}
for filename in json_data['pick_support_surface_file_names']:
obstacle_from_name[strip_extension(filename)] = \
create_obj(os.path.join(obj_directory, filename), scale=scale, color=(0.5, 0.5, 0.5, 1))
for filename in json_data['place_support_surface_file_names']:
obstacle_from_name[strip_extension(filename)] = \
create_obj(os.path.join(obj_directory, filename), scale=scale, color=(1., 0., 0., 1))
brick_from_index = {}
for json_element in json_data['sequenced_elements']:
index = json_element['order_id']
pick_body = create_obj(os.path.join(
obj_directory, json_element['pick_element_geometry_file_name']),
scale=scale,
color=(0, 0, 1, 1))
add_body_name(pick_body, index)
#place_body = create_obj(os.path.join(obj_directory, json_element['place_element_geometry_file_name']),
# scale=scale, color=(0, 1, 0, 1))
#add_body_name(place_body, name)
world_from_obj_pick = get_pose(pick_body)
# [u'pick_grasp_plane', u'pick_grasp_retreat_plane', u'place_grasp_retreat_plane',
# u'pick_grasp_approach_plane', u'place_grasp_approach_plane', u'place_grasp_plane']
ee_grasp_poses = [{
name: pose_from_tform(parse_transform(approach))
for name, approach in json_grasp.items()
} for json_grasp in json_element['grasps']]
# pick_grasp_plane is at the top of the object with z facing downwards
grasp_index = 0
world_from_ee_pick = ee_grasp_poses[grasp_index]['pick_grasp_plane']
world_from_ee_place = ee_grasp_poses[grasp_index]['place_grasp_plane']
#draw_pose(world_from_ee_pick, length=0.04)
#draw_pose(world_from_ee_place, length=0.04)
ee_from_obj = multiply(invert(world_from_ee_pick),
world_from_obj_pick) # Using pick frame
world_from_obj_place = multiply(world_from_ee_place, ee_from_obj)
#set_pose(pick_body, world_from_obj_place)
grasps = [
Grasp(
index, num, *[
multiply(invert(world_from_ee_pick[name]),
world_from_obj_pick) for name in GRASP_NAMES
]) for num, world_from_ee_pick in enumerate(ee_grasp_poses)
]
brick_from_index[index] = Brick(
index=index,
body=pick_body,
initial_pose=WorldPose(index, world_from_obj_pick),
goal_pose=WorldPose(index, world_from_obj_place),
grasps=grasps,
goal_supports=json_element.get('place_contact_ngh_ids', []))
# pick_contact_ngh_ids are movable element contact partial orders
# pick_support_surface_file_names are fixed element contact partial orders
return robot, brick_from_index, obstacle_from_name
def main():
parser = argparse.ArgumentParser() # Automatically includes help
parser.add_argument('-viewer', action='store_true', help='enable viewer.')
args = parser.parse_args()
connect(use_gui=True)
with LockRenderer():
draw_pose(unit_pose(), length=1, width=1)
floor = create_floor()
set_point(floor, Point(z=-EPSILON))
table1 = create_table(width=TABLE_WIDTH,
length=TABLE_WIDTH / 2,
height=TABLE_WIDTH / 2,
top_color=TAN,
cylinder=False)
set_point(table1, Point(y=+0.5))
table2 = create_table(width=TABLE_WIDTH,
length=TABLE_WIDTH / 2,
height=TABLE_WIDTH / 2,
top_color=TAN,
cylinder=False)
set_point(table2, Point(y=-0.5))
tables = [table1, table2]
#set_euler(table1, Euler(yaw=np.pi/2))
with HideOutput():
# data_path = add_data_path()
# robot_path = os.path.join(data_path, WSG_GRIPPER)
robot_path = get_model_path(
WSG_50_URDF) # WSG_50_URDF | PANDA_HAND_URDF
robot = load_pybullet(robot_path, fixed_base=True)
#dump_body(robot)
block1 = create_box(w=BLOCK_SIDE,
l=BLOCK_SIDE,
h=BLOCK_SIDE,
color=RED)
block_z = stable_z(block1, table1)
set_point(block1, Point(y=-0.5, z=block_z))
block2 = create_box(w=BLOCK_SIDE,
l=BLOCK_SIDE,
h=BLOCK_SIDE,
color=GREEN)
set_point(block2, Point(x=-0.25, y=-0.5, z=block_z))
block3 = create_box(w=BLOCK_SIDE,
l=BLOCK_SIDE,
h=BLOCK_SIDE,
color=BLUE)
set_point(block3, Point(x=-0.15, y=+0.5, z=block_z))
blocks = [block1, block2, block3]
set_camera_pose(camera_point=Point(x=-1, z=block_z + 1),
target_point=Point(z=block_z))
block_pose = get_pose(block1)
open_gripper(robot)
tool_link = link_from_name(robot, 'tool_link')
base_from_tool = get_relative_pose(robot, tool_link)
#draw_pose(unit_pose(), parent=robot, parent_link=tool_link)
grasps = get_side_grasps(block1,
tool_pose=Pose(euler=Euler(yaw=np.pi / 2)),
top_offset=0.02,
grasp_length=0.03,
under=False)[1:2]
for grasp in grasps:
gripper_pose = multiply(block_pose, invert(grasp))
set_pose(robot, multiply(gripper_pose, invert(base_from_tool)))
wait_for_user()
wait_for_user('Finish?')
disconnect()
def main(partial=False, defer=False, verbose=True):
parser = create_parser()
parser.add_argument('-enable',
action='store_true',
help='Enables rendering during planning')
parser.add_argument('-teleport',
action='store_true',
help='Teleports between configurations')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
connect(use_gui=args.viewer)
problem_fn = cooking_problem
# holding_problem | stacking_problem | cleaning_problem | cooking_problem
# cleaning_button_problem | cooking_button_problem
with HideOutput():
problem = problem_fn()
#state_id = save_state()
saver = WorldSaver()
#dump_world()
pddlstream_problem = pddlstream_from_problem(problem,
teleport=args.teleport)
stream_info = {
# 'test-cfree-pose-pose': StreamInfo(p_success=1e-3, verbose=verbose),
# 'test-cfree-approach-pose': StreamInfo(p_success=1e-2, verbose=verbose),
# 'test-cfree-traj-pose': StreamInfo(p_success=1e-1, verbose=verbose),
'MoveCost': FunctionInfo(opt_move_cost_fn),
}
stream_info.update({
'sample-pose':
StreamInfo(PartialInputs('?r')),
'inverse-kinematics':
StreamInfo(PartialInputs('?p')),
'plan-base-motion':
StreamInfo(PartialInputs('?q1 ?q2'),
defer_fn=defer_shared if defer else never_defer),
} if partial else {
'sample-pose': StreamInfo(from_fn(opt_pose_fn)),
'inverse-kinematics': StreamInfo(from_fn(opt_ik_fn)),
'plan-base-motion': StreamInfo(from_fn(opt_motion_fn)),
})
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
print('Streams:', str_from_object(set(stream_map)))
print(SEPARATOR)
with Profiler():
with LockRenderer(lock=not args.enable):
solution = solve(pddlstream_problem,
algorithm=args.algorithm,
unit_costs=args.unit,
stream_info=stream_info,
success_cost=INF,
verbose=True,
debug=False)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
print(SEPARATOR)
with LockRenderer(lock=not args.enable):
commands = post_process(problem, plan)
problem.remove_gripper()
saver.restore()
#restore_state(state_id)
saver.restore()
wait_if_gui('Execute?')
if args.simulate:
control_commands(commands)
else:
apply_commands(State(), commands, time_step=0.01)
wait_if_gui('Finish?')
disconnect()
def main():
parser = create_parser()
parser.add_argument('-problem', default='problem1', help='The name of the problem to solve')
parser.add_argument('-cfree', action='store_true', help='Disables collisions')
parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time')
parser.add_argument('-enable', action='store_true', help='Enables rendering during planning')
parser.add_argument('-teleport', action='store_true', help='Teleports between configurations')
parser.add_argument('-simulate', action='store_true', help='Simulates the system')
parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_fn_from_name:
raise ValueError(args.problem)
problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn()
saver = WorldSaver()
draw_base_limits(problem.limits, color=RED)
pddlstream_problem = pddlstream_from_problem(problem, collisions=not args.cfree, teleport=args.teleport)
stream_info = {
'inverse-kinematics': StreamInfo(),
'plan-base-motion': StreamInfo(overhead=1e1),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
#print('Streams:', stream_map.keys())
success_cost = 0 if args.optimal else INF
planner = 'ff-astar'
search_sample_ratio = 1
max_planner_time = 10
with Profiler(field='cumtime', num=25): # cumtime | tottime
with LockRenderer(lock=not args.enable):
solution = solve(pddlstream_problem, stream_info=stream_info,
planner=planner, max_planner_time=max_planner_time,
unit_costs=args.unit, success_cost=success_cost,
max_time=args.max_time, verbose=True, debug=False,
unit_efforts=True, effort_weight=1,
search_sample_ratio=search_sample_ratio)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
# Maybe openrave didn't actually sample any joints...
# http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/
with LockRenderer(lock=not args.enable):
commands = post_process(problem, plan, teleport=args.teleport)
saver.restore()
if args.simulate:
control_commands(commands)
else:
time_step = None if args.teleport else 0.01
apply_commands(BeliefState(problem), commands, time_step)
wait_for_user()
disconnect()
def main(display=True, teleport=False, partial=False, defer=False):
parser = argparse.ArgumentParser()
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='enable the viewer while planning')
#parser.add_argument('-display', action='store_true', help='displays the solution')
args = parser.parse_args()
connect(use_gui=args.viewer)
problem_fn = cooking_problem
# holding_problem | stacking_problem | cleaning_problem | cooking_problem
# cleaning_button_problem | cooking_button_problem
with HideOutput():
problem = problem_fn()
state_id = save_state()
#saved_world = WorldSaver()
#dump_world()
pddlstream_problem = pddlstream_from_problem(problem, teleport=teleport)
stream_info = {
'sample-pose':
StreamInfo(PartialInputs('?r')),
'inverse-kinematics':
StreamInfo(PartialInputs('?p')),
'plan-base-motion':
StreamInfo(PartialInputs('?q1 ?q2'),
defer_fn=defer_shared if defer else never_defer),
'MoveCost':
FunctionInfo(opt_move_cost_fn),
} if partial else {
'sample-pose': StreamInfo(from_fn(opt_pose_fn)),
'inverse-kinematics': StreamInfo(from_fn(opt_ik_fn)),
'plan-base-motion': StreamInfo(from_fn(opt_motion_fn)),
'MoveCost': FunctionInfo(opt_move_cost_fn),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
print('Streams:', stream_map.keys())
pr = cProfile.Profile()
pr.enable()
with LockRenderer():
#solution = solve_incremental(pddlstream_problem, debug=True)
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
success_cost=INF,
debug=False)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
if plan is None:
return
if (not display) or (plan is None):
disconnect()
return
with LockRenderer():
commands = post_process(problem, plan)
if args.viewer:
restore_state(state_id)
else:
disconnect()
connect(use_gui=True)
with HideOutput():
problem_fn() # TODO: way of doing this without reloading?
if args.simulate:
control_commands(commands)
else:
apply_commands(State(), commands, time_step=0.01)
user_input('Finish?')
disconnect()
def main(display=True, teleport=False):
parser = argparse.ArgumentParser()
#parser.add_argument('-problem', default='rovers1', help='The name of the problem to solve')
parser.add_argument('-algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-t',
'--max_time',
default=120,
type=int,
help='The max time')
parser.add_argument('-unit', action='store_true', help='Uses unit costs')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='enable the viewer while planning')
args = parser.parse_args()
print(args)
#problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
#if args.problem not in problem_fn_from_name:
# raise ValueError(args.problem)
#problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn(collisions=not args.cfree)
saver = WorldSaver()
draw_base_limits(problem.limits, color=RED)
pddlstream_problem = pddlstream_from_problem(problem, teleport=teleport)
stream_info = {
'test-cfree-conf-pose': StreamInfo(negate=True, p_success=1e-2),
'test-cfree-traj-pose': StreamInfo(negate=True, p_success=1e-1),
'compute-motion': StreamInfo(eager=True, p_success=0),
'test-reachable': StreamInfo(eager=True),
'Distance': FunctionInfo(eager=True),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
success_cost = 0 if args.optimal else INF
planner = 'ff-wastar1'
search_sample_ratio = 0
max_planner_time = 10
pr = cProfile.Profile()
pr.enable()
with LockRenderer(True):
if args.algorithm == 'focused':
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
planner=planner,
max_planner_time=max_planner_time,
debug=False,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True,
unit_efforts=True,
effort_weight=1,
bind=True,
max_skeletons=None,
search_sample_ratio=search_sample_ratio)
elif args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
planner=planner,
max_planner_time=max_planner_time,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True)
else:
raise ValueError(args.algorithm)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime
if plan is None:
wait_for_user()
return
if (not display) or (plan is None):
disconnect()
return
with LockRenderer():
commands = post_process(problem, plan, teleport=teleport)
saver.restore() # Assumes bodies are ordered the same way
if not args.viewer:
disconnect()
connect(use_gui=True)
with LockRenderer():
with HideOutput():
problem_fn() # TODO: way of doing this without reloading?
saver.restore() # Assumes bodies are ordered the same way
wait_for_user()
if args.simulate:
control_commands(commands)
else:
time_step = None if teleport else 0.01
apply_commands(BeliefState(problem), commands, time_step=time_step)
wait_for_user()
disconnect()
def main():
parser = create_parser(default_algorithm='binding')
parser.add_argument('-cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-t',
'--max_time',
default=120,
type=int,
help='The max time')
parser.add_argument('-enable',
action='store_true',
help='Enables rendering during planning')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn(collisions=not args.cfree)
saver = WorldSaver()
draw_base_limits(problem.limits, color=RED)
pddlstream_problem = pddlstream_from_problem(problem)
stream_info = {
'test-cfree-conf-pose': StreamInfo(p_success=1e-2),
'test-cfree-traj-pose': StreamInfo(p_success=1e-1),
'compute-motion': StreamInfo(eager=True, p_success=0),
'test-reachable': StreamInfo(eager=True),
'Distance': FunctionInfo(eager=True),
}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
success_cost = 0 if args.optimal else INF
planner = 'ff-wastar1'
search_sample_ratio = 0
max_planner_time = 10
with Profiler(field='tottime', num=25): # cumtime | tottime
with LockRenderer(lock=not args.enable):
solution = solve(pddlstream_problem,
algorithm=args.algorithm,
stream_info=stream_info,
planner=planner,
max_planner_time=max_planner_time,
debug=False,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True,
unit_efforts=True,
effort_weight=1,
search_sample_ratio=search_sample_ratio)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
with LockRenderer():
commands = post_process(problem, plan)
saver.restore() # Assumes bodies are ordered the same way
wait_for_user()
if args.simulate:
control_commands(commands)
else:
apply_commands(BeliefState(problem), commands,
time_step=1e-2) # 1e-2 | None
wait_for_user()
disconnect()
def main(verbose=True):
# TODO: could work just on postprocessing
# TODO: try the other reachability database
# TODO: option to only consider costs during local optimization
parser = create_parser()
parser.add_argument('-problem',
default='packed',
help='The name of the problem to solve')
parser.add_argument('-n',
'--number',
default=5,
type=int,
help='The number of objects')
parser.add_argument('-cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-t',
'--max_time',
default=120,
type=int,
help='The max time')
parser.add_argument('-teleport',
action='store_true',
help='Teleports between configurations')
parser.add_argument('-enable',
action='store_true',
help='Enables rendering during planning')
parser.add_argument('-simulate',
action='store_true',
help='Simulates the system')
parser.add_argument('-viewer',
action='store_true',
help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_fn_from_name:
raise ValueError(args.problem)
problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn(num=args.number)
draw_base_limits(problem.base_limits, color=(1, 0, 0))
saver = WorldSaver()
#handles = []
#for link in get_group_joints(problem.robot, 'left_arm'):
# handles.append(draw_link_name(problem.robot, link))
#wait_for_user()
pddlstream_problem = pddlstream_from_problem(problem,
collisions=not args.cfree,
teleport=args.teleport)
stream_info = {
'inverse-kinematics':
StreamInfo(),
'plan-base-motion':
StreamInfo(overhead=1e1),
'test-cfree-pose-pose':
StreamInfo(p_success=1e-3, verbose=verbose),
'test-cfree-approach-pose':
StreamInfo(p_success=1e-2, verbose=verbose),
'test-cfree-traj-pose':
StreamInfo(p_success=1e-1,
verbose=verbose), # TODO: apply to arm and base trajs
#'test-cfree-traj-grasp-pose': StreamInfo(verbose=verbose),
'Distance':
FunctionInfo(p_success=0.99, opt_fn=lambda q1, q2: BASE_CONSTANT),
#'MoveCost': FunctionInfo(lambda t: BASE_CONSTANT),
}
#stream_info = {}
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
print('Streams:', str_from_object(set(stream_map)))
success_cost = 0 if args.optimal else INF
planner = 'ff-astar' if args.optimal else 'ff-wastar3'
search_sample_ratio = 2
max_planner_time = 10
# effort_weight = 0 if args.optimal else 1
effort_weight = 1e-3 if args.optimal else 1
with Profiler(field='tottime', num=25): # cumtime | tottime
with LockRenderer(lock=not args.enable):
solution = solve(pddlstream_problem,
algorithm=args.algorithm,
stream_info=stream_info,
planner=planner,
max_planner_time=max_planner_time,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=True,
debug=False,
unit_efforts=True,
effort_weight=effort_weight,
search_sample_ratio=search_sample_ratio)
saver.restore()
cost_over_time = [(s.cost, s.time) for s in SOLUTIONS]
for i, (cost, runtime) in enumerate(cost_over_time):
print('Plan: {} | Cost: {:.3f} | Time: {:.3f}'.format(
i, cost, runtime))
#print(SOLUTIONS)
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
with LockRenderer(lock=not args.enable):
commands = post_process(problem, plan, teleport=args.teleport)
saver.restore()
draw_base_limits(problem.base_limits, color=(1, 0, 0))
wait_for_user()
if args.simulate:
control_commands(commands)
else:
time_step = None if args.teleport else 0.01
apply_commands(State(), commands, time_step)
wait_for_user()
disconnect()
def main(display=True, simulate=False, teleport=False):
parser = argparse.ArgumentParser()
parser.add_argument('-viewer',
action='store_true',
help='enable the viewer while planning')
#parser.add_argument('-display', action='store_true', help='displays the solution')
args = parser.parse_args()
connect(use_gui=args.viewer)
problem_fn = cooking_problem
# holding_problem | stacking_problem | cleaning_problem | cooking_problem
# cleaning_button_problem | cooking_button_problem
with HideOutput():
problem = problem_fn()
state_id = save_state()
#saved_world = WorldSaver()
#dump_world()
pddlstream_problem = pddlstream_from_problem(problem, teleport=teleport)
stream_info = {
'sample-pose': StreamInfo(PartialInputs('?r')),
'inverse-kinematics': StreamInfo(PartialInputs('?p')),
'plan-base-motion': StreamInfo(PartialInputs('?q1 ?q2')),
'MoveCost': FunctionInfo(opt_move_cost_fn),
}
synthesizers = [
StreamSynthesizer(
'safe-base-motion', {
'plan-base-motion': 1,
'TrajPoseCollision': 0,
'TrajGraspCollision': 0,
'TrajArmCollision': 0,
}, from_fn(get_base_motion_synth(problem, teleport))),
] if USE_SYNTHESIZERS else []
_, _, _, stream_map, init, goal = pddlstream_problem
print('Init:', init)
print('Goal:', goal)
print('Streams:', stream_map.keys())
print('Synthesizers:', synthesizers)
pr = cProfile.Profile()
pr.enable()
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
synthesizers=synthesizers,
success_cost=INF)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
if plan is None:
return
if (not display) or (plan is None):
disconnect()
return
commands = post_process(problem, plan)
if args.viewer:
restore_state(state_id)
else:
disconnect()
connect(use_gui=True)
with HideOutput():
problem_fn() # TODO: way of doing this without reloading?
if simulate:
enable_gravity()
control_commands(commands)
else:
step_commands(commands, time_step=0.01)
user_input('Finish?')
disconnect()
def main(display=True, teleport=False):
parser = argparse.ArgumentParser()
parser.add_argument('-algorithm', default='incremental', help='Specifies the algorithm')
parser.add_argument('-cfree', action='store_true', help='Disables collisions')
parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-t', '--max_time', default=5*60, type=int, help='The max time')
parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning')
args = parser.parse_args()
print(args)
#problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS}
#if args.problem not in problem_fn_from_name:
# raise ValueError(args.problem)
#problem_fn = problem_fn_from_name[args.problem]
connect(use_gui=args.viewer)
with HideOutput():
problem = problem_fn(collisions=not args.cfree)
saver = WorldSaver()
draw_base_limits(problem.limits, color=RED)
pddlstream, edges = pddlstream_from_problem(problem, teleport=teleport)
_, constant_map, _, stream_map, init, goal = pddlstream
print('Constants:', constant_map)
print('Init:', init)
print('Goal:', goal)
success_cost = 0 if args.optimal else INF
max_planner_time = 10
stream_info = {
'compute-motion': StreamInfo(eager=True, p_success=0),
'ConfConfCollision': FunctionInfo(p_success=1, overhead=0.1),
'TrajConfCollision': FunctionInfo(p_success=1, overhead=1),
'TrajTrajCollision': FunctionInfo(p_success=1, overhead=10),
'TrajDistance': FunctionInfo(eager=True), # Need to eagerly evaluate otherwise 0 duration (failure)
}
pr = cProfile.Profile()
pr.enable()
with LockRenderer(False):
if args.algorithm == 'incremental':
solution = solve_incremental(pddlstream,
max_planner_time=max_planner_time,
success_cost=success_cost, max_time=args.max_time,
start_complexity=INF,
verbose=True, debug=True)
elif args.algorithm == 'focused':
solution = solve_focused(pddlstream, stream_info=stream_info,
max_planner_time=max_planner_time,
success_cost=success_cost, max_time=args.max_time,
max_skeletons=None, bind=True, max_failures=INF,
verbose=True, debug=True)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime
if plan is None:
wait_for_user()
return
if (not display) or (plan is None):
disconnect()
return
if not args.viewer:
disconnect()
connect(use_gui=True)
with LockRenderer():
with HideOutput():
problem_fn() # TODO: way of doing this without reloading?
saver.restore() # Assumes bodies are ordered the same way
draw_edges(edges)
state = BeliefState(problem)
wait_for_user()
#time_step = None if teleport else 0.01
#with VideoSaver('video.mp4'):
display_plan(problem, state, plan)
wait_for_user()
disconnect()