def pddlstream_from_problem(robot,
movable=[],
teleport=False,
movable_collisions=False,
grasp_name='top'):
#assert (not are_colliding(tree, kin_cache))
domain_pddl = read(get_file_path(__file__, 'domain.pddl'))
stream_pddl = read(get_file_path(__file__, 'stream.pddl'))
constant_map = {}
print('Robot:', robot)
conf = BodyConf(robot, get_configuration(robot))
init = [('CanMove', ), ('Conf', conf), ('AtConf', conf), ('HandEmpty', )]
fixed = get_fixed(robot, movable)
print('Movable:', movable)
print('Fixed:', fixed)
for body in movable:
pose = BodyPose(body, get_pose(body))
init += [('Graspable', body), ('Pose', body, pose),
('AtPose', body, pose)]
for surface in fixed:
init += [('Stackable', body, surface)]
if is_placement(body, surface):
init += [('Supported', body, pose, surface)]
for body in fixed:
name = get_body_name(body)
if 'sink' in name:
init += [('Sink', body)]
if 'stove' in name:
init += [('Stove', body)]
body = movable[0]
goal = (
'and',
('AtConf', conf),
#('Holding', body),
#('On', body, fixed[1]),
#('On', body, fixed[2]),
#('Cleaned', body),
('Cooked', body),
)
stream_map = {
'sample-pose': from_gen_fn(get_stable_gen(fixed)),
'sample-grasp': from_gen_fn(get_grasp_gen(robot, grasp_name)),
'inverse-kinematics': from_fn(get_ik_fn(robot, fixed, teleport)),
#'plan-free-motion': from_fn(get_free_motion_gen(robot, fixed, teleport)),
'plan-free-motion': empty_gen(),
# 'plan-holding-motion': from_fn(get_holding_motion_gen(robot, fixed, teleport)),
'plan-holding-motion': empty_gen(),
'TrajCollision': get_movable_collision_test(),
}
return domain_pddl, constant_map, stream_pddl, stream_map, init, goal
def pddlstream_from_problem(problem, teleport=False, movable_collisions=False):
robot = problem.robot
domain_pddl = read(get_file_path(__file__, 'domain.pddl'))
stream_pddl = read(get_file_path(__file__, 'stream.pddl'))
constant_map = {
'world': 'world',
}
world = 'world'
initial_bq = Pose(robot, get_pose(robot))
init = [
('CanMove',),
('BConf', initial_bq), # TODO: could make pose as well...
('AtBConf', initial_bq),
('AtAConf', world, None),
('AtPose', world, world, None),
] + [('Sink', s) for s in problem.sinks] + \
[('Stove', s) for s in problem.stoves] + \
[('Connected', b, d) for b, d in problem.buttons] + \
[('Button', b) for b, _ in problem.buttons]
#for arm in ARM_JOINT_NAMES:
for arm in problem.arms:
joints = get_arm_joints(robot, arm)
conf = Conf(robot, joints, get_joint_positions(robot, joints))
init += [('Arm', arm), ('AConf', arm, conf),
('HandEmpty', arm), ('AtAConf', arm, conf), ('AtPose', arm, arm, None)]
if arm in problem.arms:
init += [('Controllable', arm)]
for body in problem.movable:
pose = Pose(body, get_pose(body))
init += [('Graspable', body), ('Pose', body, pose),
('AtPose', world, body, pose)]
for surface in problem.surfaces:
init += [('Stackable', body, surface)]
if is_placement(body, surface):
init += [('Supported', body, pose, surface)]
goal = ['and']
if problem.goal_conf is not None:
goal_conf = Pose(robot, problem.goal_conf)
init += [('BConf', goal_conf)]
goal += [('AtBConf', goal_conf)]
goal += [('Holding', a, b) for a, b in problem.goal_holding] + \
[('On', b, s) for b, s in problem.goal_on] + \
[('Cleaned', b) for b in problem.goal_cleaned] + \
[('Cooked', b) for b in problem.goal_cooked]
stream_map = {
'sample-pose': get_stable_gen(problem),
'sample-grasp': from_list_fn(get_grasp_gen(problem)),
'inverse-kinematics': from_gen_fn(get_ik_ir_gen(problem, teleport=teleport)),
'plan-base-motion': from_fn(get_motion_gen(problem, teleport=teleport)),
#'plan-base-motion': empty_gen(),
'BTrajCollision': fn_from_constant(False),
}
# get_press_gen(problem, teleport=teleport)
return domain_pddl, constant_map, stream_pddl, stream_map, init, goal
def create_problem(goal, obstacles=(), regions={}, max_distance=.5):
directory = os.path.dirname(os.path.abspath(__file__))
domain_pddl = read(os.path.join(directory, 'domain.pddl'))
stream_pddl = read(os.path.join(directory, 'stream.pddl'))
constant_map = {}
q0 = np.array([0, 0])
init = [
('Conf', q0),
('AtConf', q0),
] + [('Region', r) for r in regions]
if isinstance(goal, str):
goal = ('In', goal)
else:
init += [('Conf', goal)]
goal = ('AtConf', goal)
np.set_printoptions(precision=3)
samples = []
def region_gen(region):
while True:
q = sample_box(regions[region])
samples.append(q)
yield (q, )
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf
# d = 2
# vol_free = (1 - 0) * (1 - 0)
# vol_ball = math.pi * (1 ** 2)
# gamma = 2 * ((1 + 1. / d) * (vol_free / vol_ball)) ** (1. / d)
roadmap = []
def connected_test(q1, q2):
# n = len(samples)
# threshold = gamma * (math.log(n) / n) ** (1. / d)
threshold = max_distance
are_connected = (get_distance(q1, q2) <= threshold) and \
is_collision_free((q1, q2), obstacles)
if are_connected:
roadmap.append((q1, q2))
return are_connected
def distance_fn(q1, q2):
return scale_distance(get_distance(q1, q2))
stream_map = {
'sample-region': from_gen_fn(region_gen),
'connect': from_test(connected_test),
'distance': distance_fn,
}
problem = (domain_pddl, constant_map, stream_pddl, stream_map, init, goal)
return problem, roadmap
def pddlstream_from_tamp(tamp_problem):
initial = tamp_problem.initial
assert (initial.holding is None)
known_poses = list(initial.block_poses.values()) + \
list(tamp_problem.goal_poses.values())
directory = os.path.dirname(os.path.abspath(__file__))
domain_pddl = read(os.path.join(directory, 'domain.pddl'))
stream_pddl = read(os.path.join(directory, 'stream.pddl'))
q100 = np.array([100, 100])
constant_map = {
'q100': q100,
}
init = [
#Type(q100, 'conf'),
('CanMove',),
('Conf', q100),
('Conf', initial.conf),
('AtConf', initial.conf),
('HandEmpty',),
Equal((TOTAL_COST,), 0)] + \
[('Block', b) for b in initial.block_poses.keys()] + \
[('Pose', p) for p in known_poses] + \
[('AtPose', b, p) for b, p in initial.block_poses.items()]
# [('Pose', p) for p in known_poses + tamp_problem.poses] + \
goal = And(*[('AtPose', b, p) for b, p in tamp_problem.goal_poses.items()])
def collision_test(p1, p2):
return np.linalg.norm(p2 - p1) <= 1e-1
def distance_fn(q1, q2):
ord = 1 # 1 | 2
return int(math.ceil(np.linalg.norm(q2 - q1, ord=ord)))
# TODO: convert to lower case
stream_map = {
#'sample-pose': from_gen_fn(lambda: ((np.array([x, 0]),) for x in range(len(poses), n_poses))),
'sample-pose':
from_gen_fn(lambda: ((p, ) for p in tamp_problem.poses)),
'inverse-kinematics': from_fn(lambda p: (p + GRASP, )),
#'inverse-kinematics': IKGenerator,
#'inverse-kinematics': IKFactGenerator,
'collision-free': from_test(lambda *args: not collision_test(*args)),
'collision': collision_test,
#'constraint-solver': None,
'distance': distance_fn,
}
return domain_pddl, constant_map, stream_pddl, stream_map, init, goal
def pddlstream_from_tamp(tamp_problem):
initial = tamp_problem.initial
assert (initial.holding is None)
domain_pddl = read(get_file_path(__file__, 'domain.pddl'))
stream_pddl = read(get_file_path(__file__, 'stream.pddl'))
constant_map = {}
init = [
('CanMove',),
('Conf', initial.conf),
('AtConf', initial.conf),
('HandEmpty',),
Equal((TOTAL_COST,), 0)] + \
[('Block', b) for b in initial.block_poses.keys()] + \
[('Pose', b, p) for b, p in initial.block_poses.items()] + \
[('Region', r) for r in tamp_problem.regions.keys()] + \
[('AtPose', b, p) for b, p in initial.block_poses.items()] + \
[('Placeable', b, GROUND) for b in initial.block_poses.keys()] + \
[('Placeable', b, r) for b, r in tamp_problem.goal_regions.items()]
goal_literals = [('HandEmpty',)] + \
[('In', b, r) for b, r in tamp_problem.goal_regions.items()]
if tamp_problem.goal_conf is not None:
goal_literals += [('AtConf', tamp_problem.goal_conf)]
goal = And(*goal_literals)
stream_map = {
'plan-motion': from_fn(plan_motion),
'sample-pose': from_gen_fn(get_pose_gen(tamp_problem.regions)),
'test-region': from_test(get_region_test(tamp_problem.regions)),
'inverse-kinematics': from_fn(inverse_kin_fn),
'collision-free': from_test(lambda *args: not collision_test(*args)),
'cfree': lambda *args: not collision_test(*args),
'posecollision': collision_test,
'trajcollision': lambda *args: False,
'distance': distance_fn,
#'Valid': valid_state_fn,
}
#stream_map = 'debug'
return domain_pddl, constant_map, stream_pddl, stream_map, init, goal
def get_problem(init, goal):
directory = os.path.dirname(os.path.abspath(__file__))
domain_pddl = read_pddl('stream/domain.pddl')
constant_map = {}
stream_pddl = read(os.path.join(directory, 'stream', 'stream.pddl'))
east_map = dict()
for pred in init:
if pred[0] == 'east':
east_map[pred[1]] = pred[2]
def gen_far_east(p, tile):
current_far_east = test_simple_and_derived
while current_far_east in east_map:
print(current_far_east)
next_east = east_map[current_far_east]
current_far_east = next_east
yield (next_east, )
stream_map = {
'find-far-east': from_gen_fn(gen_far_east),
}
return domain_pddl, constant_map, stream_pddl, stream_map, init, goal
def pddlstream_from_state(state, teleport=False):
task = state.task
robot = task.robot
# TODO: infer open world from task
domain_pddl = read(get_file_path(__file__, 'domain.pddl'))
stream_pddl = read(get_file_path(__file__, 'stream.pddl'))
constant_map = {
'base': 'base',
'left': 'left',
'right': 'right',
'head': 'head',
}
#base_conf = state.poses[robot]
base_conf = Conf(robot, get_group_joints(robot, 'base'),
get_group_conf(robot, 'base'))
scan_cost = 1
init = [
('BConf', base_conf),
('AtBConf', base_conf),
Equal(('MoveCost', ), scale_cost(1)),
Equal(('PickCost', ), scale_cost(1)),
Equal(('PlaceCost', ), scale_cost(1)),
Equal(('ScanCost', ), scale_cost(scan_cost)),
Equal(('RegisterCost', ), scale_cost(1)),
]
holding_arms = set()
holding_bodies = set()
for attach in state.attachments.values():
holding_arms.add(attach.arm)
holding_bodies.add(attach.body)
init += [('Grasp', attach.body, attach.grasp),
('AtGrasp', attach.arm, attach.body, attach.grasp)]
for arm in ARM_NAMES:
joints = get_arm_joints(robot, arm)
conf = Conf(robot, joints, get_joint_positions(robot, joints))
init += [('Arm', arm), ('AConf', arm, conf), ('AtAConf', arm, conf)]
if arm in task.arms:
init += [('Controllable', arm)]
if arm not in holding_arms:
init += [('HandEmpty', arm)]
for body in task.get_bodies():
if body in holding_bodies:
continue
# TODO: no notion whether observable actually corresponds to the correct thing
pose = state.poses[body]
init += [
('Pose', body, pose),
('AtPose', body, pose),
('Observable', pose),
]
for body in task.movable:
init += [('Graspable', body)]
for body in task.get_bodies():
supports = task.get_supports(body)
if supports is None:
continue
for surface in supports:
p_obs = state.b_on[body].prob(surface)
cost = revisit_mdp_cost(0, scan_cost,
p_obs) # TODO: imperfect observation model
init += [('Stackable', body, surface),
Equal(('LocalizeCost', surface, body), scale_cost(cost))]
#if is_placement(body, surface):
if is_center_stable(body, surface):
if body in holding_bodies:
continue
pose = state.poses[body]
init += [('Supported', body, pose, surface)]
for body in task.get_bodies():
if state.is_localized(body):
init.append(('Localized', body))
else:
init.append(('Uncertain', body))
if body in state.registered:
init.append(('Registered', body))
goal = And(*[('Holding', a, b) for a, b in task.goal_holding] + \
[('On', b, s) for b, s in task.goal_on] + \
[('Localized', b) for b in task.goal_localized] + \
[('Registered', b) for b in task.goal_registered])
stream_map = {
'sample-pose': get_stable_gen(task),
'sample-grasp': from_list_fn(get_grasp_gen(task)),
'inverse-kinematics':
from_gen_fn(get_ik_ir_gen(task, teleport=teleport)),
'plan-base-motion': from_fn(get_motion_gen(task, teleport=teleport)),
'inverse-visibility': from_gen_fn(get_vis_gen(task)),
'plan-scan': from_gen_fn(get_scan_gen(state)),
}
return Problem(domain_pddl, constant_map, stream_pddl, stream_map, init,
goal)