def compile_problem(tamp_problem):
"""
Constructs a STRIPStream problem for the countable TMP problem.
:param tamp_problem: a :class:`.TMPProblem`
:return: a :class:`.STRIPStreamProblem`
"""
B1, B2 = Param(BLOCK), Param(BLOCK)
P1, P2 = Param(POSE), Param(POSE)
Q1, Q2 = Param(CONF), Param(CONF)
actions = [
Action(name='pick',
parameters=[B1, P1, Q1],
condition=And(AtPose(B1, P1), HandEmpty(), AtConf(Q1),
LegalKin(P1, Q1)),
effect=And(Holding(B1), Not(AtPose(B1, P1)), Not(HandEmpty()))),
Action(name='place',
parameters=[B1, P1, Q1],
condition=And(Holding(B1), AtConf(Q1), LegalKin(P1, Q1),
ForAll([B2], Or(Equal(B1, B2), Safe(B2, P1)))),
effect=And(AtPose(B1, P1), HandEmpty(), Not(Holding(B1)))),
Action(name='move',
parameters=[Q1, Q2],
condition=AtConf(Q1),
effect=And(AtConf(Q2), Not(AtConf(Q1)))),
]
axioms = [
Axiom(effect=Safe(B2, P1),
condition=Exists([P2], And(AtPose(B2, P2), CollisionFree(P1,
P2)))),
]
cond_streams = [
EasyGenStream(inputs=[],
outputs=[P1],
conditions=[],
effects=[],
generator=lambda: irange(0, NUM_POSES)),
EasyGenStream(inputs=[P1],
outputs=[Q1],
conditions=[],
effects=[LegalKin(P1, Q1)],
generator=lambda p: iter([p])),
EasyTestStream(inputs=[P1, P2],
conditions=[],
effects=[CollisionFree(P1, P2)],
test=lambda p1, p2: p1 != p2,
eager=EAGER_TESTS),
]
constants = []
initial_atoms = [
AtConf(tamp_problem.initial_config),
] + [
AtPose(block, pose)
for block, pose in tamp_problem.initial_poses.iteritems()
]
if tamp_problem.initial_holding is False:
initial_atoms.append(HandEmpty())
else:
initial_atoms.append(Holding(tamp_problem.initial_holding, BLOCK))
goal_literals = []
if tamp_problem.goal_holding is False:
goal_literals.append(HandEmpty())
elif tamp_problem.goal_holding is not None:
goal_literals.append(Holding(tamp_problem.goal_holding))
for block, goal in tamp_problem.goal_poses.iteritems():
goal_literals.append(AtPose(block, goal))
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, constants)
def solve_tamp(env, use_focused):
use_viewer = env.GetViewer() is not None
problem = PROBLEM(env)
# TODO: most of my examples have literally had straight-line motions plans
robot, manipulator, base_manip, ir_model = initialize_openrave(
env, ARM, min_delta=MIN_DELTA)
set_manipulator_conf(ir_model.manip, CARRY_CONFIG)
bodies = {obj: env.GetKinBody(obj) for obj in problem.movable_names}
surfaces = {surface.name: surface for surface in problem.surfaces}
open_gripper(manipulator)
initial_q = Conf(robot.GetConfigurationValues())
initial_poses = {
name: Pose(name, get_pose(body))
for name, body in bodies.iteritems()
}
# TODO: just keep track of the movable degrees of freedom
# GetActiveDOFIndices, GetActiveJointIndices, GetActiveDOFValues
saver = EnvironmentStateSaver(env)
#cfree_pose = cfree_pose_fn(env, bodies)
#cfree_traj = cfree_traj_fn(env, robot, manipulator, body1, body2, all_bodies)
#base_generator = base_generator_fn(ir_model)
#base_values = base_values_from_full_config(initial_q.value)
#goal_values = full_config_from_base_values(base_values + np.array([1, 0, 0]), initial_q.value)
#goal_conf = Conf(goal_values)
#return
####################
# TODO - should objects contain their geometry
cond_streams = [
EasyListGenStream(inputs=[O, P, G],
outputs=[Q, T],
conditions=[IsPose(O, P),
IsGrasp(O, G)],
effects=[GraspMotion(O, P, G, Q, T)],
generator=sample_grasp_traj_fn(
base_manip, ir_model, bodies, CARRY_CONFIG)),
EasyGenStream(inputs=[Q, Q2],
outputs=[T],
conditions=[],
effects=[FreeMotion(Q, Q2, T)],
generator=sample_free_base_motion_fn(base_manip, bodies),
order=1,
max_level=0),
EasyTestStream(
inputs=[O, P, O2, P2],
conditions=[IsPose(O, P), IsPose(O2, P2)],
effects=[CFreePose(P, P2)],
test=lambda *args: True, #cfree_pose,
eager=True),
EasyTestStream(inputs=[T, P],
conditions=[],
effects=[CFreeTraj(T, P)],
test=lambda *args: True),
#test=cfree_traj),
EasyListGenStream(inputs=[O],
outputs=[G],
conditions=[],
effects=[IsGrasp(O, G)],
generator=grasp_generator_fn(bodies, TOP_GRASPS,
SIDE_GRASPS,
MAX_GRASPS)),
EasyListGenStream(inputs=[O, S],
outputs=[P],
conditions=[],
effects=[IsPose(O, P), Stable(P, S)],
generator=pose_generator_fn(bodies, surfaces)),
#EasyGenStream(inputs=[O, P, G], outputs=[Q], conditions=[IsPose(O, P), IsGrasp(O, G)],
# effects=[], generator=base_generator),
]
####################
constants = []
initial_atoms = [ConfEq(initial_q), HandEmpty()]
for name in initial_poses:
initial_atoms += body_initial_atoms(name, initial_poses, bodies,
surfaces)
goal_formula = And(
ConfEq(initial_q), *[
OnSurface(obj, surface)
for obj, surface in problem.goal_surfaces.iteritems()
])
#goal_formula = ConfEq(goal_conf)
#obj, _ = problem.goal_surfaces.items()[0]
#goal_formula = And(Holding(obj))
#goal_formula = Holding(obj) # TODO: this cause a bug
stream_problem = STRIPStreamProblem(initial_atoms, goal_formula,
actions + axioms, cond_streams,
constants)
print stream_problem
handles = draw_affine_limits(robot)
if use_viewer:
for surface in problem.surfaces:
surface.draw(env)
raw_input('Start?')
max_time = INF
search_fn = get_fast_downward('eager', max_time=10, verbose=False)
if use_focused:
solve = lambda: simple_focused(stream_problem,
search=search_fn,
max_level=INF,
shared=False,
debug=False,
verbose=False,
max_time=max_time)
else:
solve = lambda: incremental_planner(stream_problem,
search=search_fn,
frequency=10,
waves=False,
debug=False,
max_time=max_time)
with env:
plan, universe = solve()
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i + 1, action, args
else:
print 'Failure'
####################
if plan is not None:
commands = process_plan(robot, bodies, plan)
if use_viewer:
print SEPARATOR
saver.Restore()
visualize_solution(commands, step=False)
raw_input('Finish?')
def compile_problem(oracle):
problem = oracle.problem
for obj in problem.goal_poses:
if problem.goal_poses[obj] == 'initial':
problem.goal_poses[obj] = oracle.initial_poses[obj]
elif problem.goal_poses[
obj] in oracle.initial_poses: # Goal names other object (TODO - compile with initial)
problem.goal_poses[obj] = oracle.initial_poses[
problem.goal_poses[obj]]
# oracle.initial_config = ??? # TODO - set the initial configuration
transit_conf = Config(oracle.default_left_arm_config)
####################
O, P, G, T = Param(OBJ), Param(POSE), Param(GRASP), Param(TRAJ)
OB, R = Param(OBJ), Param(REG)
BQ, MQ = Param(BASE_CONF), Param(MANIP_CONF)
BQ2, MQ2 = Param(BASE_CONF), Param(MANIP_CONF)
BT, MT = Param(BASE_TRAJ), Param(MANIP_TRAJ)
rename_easy(locals())
# Separate arm and base configs and trajectories?
# Make a fixed configuration for arm stuff
# If I separate arm and base poses, I can avoid worrying about collision when the base and things
# Would I ever want to actually separate these and use the same grasp at a different config?
# - Maybe for two arms?
# - I don't want to reuse trajectories at different base configs. That wouldn't make sense
# - Although if I manipulate two configs at once, maybe it would!
# - Make constant arm transit configs?
# TODO - maybe make an axiom to handle BT and MT when doing these things?
# TODO - I could equip a manip conf with a base conf at all times if I really don't want them separate
# NOTE - put then would need to update them whenever moving the base
#actions = [
# Action(name='pick', parameters=[O, P, G, BQ, MQ],
# condition=And(PoseEq(O, P), HandEmpty(), Kin(O, P, G, BQ, MQ), BaseEq(BQ), ManipEq(MQ)),
# #ForAll([OB], Or(Equal(O, OB), Safe(OB, T)))),
# effect=And(GraspEq(O, G), Not(HandEmpty()), Not(PoseEq(O, P)))),
#
# Action(name='place', parameters=[O, P, G, BQ, MQ],
# condition=And(GraspEq(O, G), Kin(O, P, G, BQ, MQ), BaseEq(BQ), ManipEq(MQ)),
# #ForAll([OB], Or(Equal(O, OB), Safe(OB, T)))),
# effect=And(PoseEq(O, P), HandEmpty(), Not(GraspEq(O, G)))),
#
# Action(name='move_arm', parameters=[MQ, MQ2, BQ, MT],
# condition=And(ManipEq(MQ), ManipMotion(MQ, MQ2, BQ, MT)),
# effect=And(ManipEq(MQ2), Not(ManipEq(MQ)))),
#
# Action(name='move_base', parameters=[BQ, BQ2, MQ, BT], # TODO - put the arm motion stuff in an axiom
# condition=And(BaseEq(BQ), TransitManip(MQ), BaseMotion(BQ, BQ2, BT)),
# effect=And(BaseEq(BQ2), Not(BaseEq(BQ)))),
# ]
# TODO - should I include the grasp in move backwards trajectory?
actions = [
abs_action(
name='pick',
parameters=[O, P, G, BQ, MQ],
conditions=[
And(PoseEq(O, P), HandEmpty(),
Kin(O, P, G, BQ,
MQ)), #ForAll([OB], Or(Equal(O, OB), Safe(OB, T)))),
And(BaseEq(BQ), ManipEq(MQ))
],
effect=And(GraspEq(O, G), Not(HandEmpty()), Not(PoseEq(O, P)))),
abs_action(
name='place',
parameters=[O, P, G, BQ, MQ],
conditions=[
And(GraspEq(O, G),
Kin(O, P, G, BQ,
MQ)), #ForAll([OB], Or(Equal(O, OB), Safe(OB, T)))),
And(BaseEq(BQ), ManipEq(MQ))
],
effect=And(PoseEq(O, P), HandEmpty(), Not(GraspEq(O, G)))),
# NOTE - the hierarchical versions of this
abs_action(
name='move_arm',
parameters=[MQ, MQ2, BQ,
MT], # TODO - Or(TransitManip(MQ), TransitManip(MQ2))
conditions=[
And(ManipEq(MQ), ManipMotion(MQ, MQ2, BQ, MT), BaseEq(BQ)),
#And(ManipEq(MQ), ManipMotion(MQ, MQ2, BQ, MT), BaseEq(BQ), Or(TransitManip(MQ), TransitManip(MQ2))) # This does something odd
#And(ManipEq(MQ), ManipMotion(MQ, MQ2, BQ, MT), BaseEq(BQ),
# Or(TransitManip(MQ), LegalConf(BQ, MQ)),
# Or(TransitManip(MQ2), LegalConf(BQ, MQ2))) # This does something odd
], # TODO - put an Or(Pair(MQ, BQ), Pair(MQ2, BQ)) or something
effect=And(ManipEq(MQ2), Not(ManipEq(MQ)))),
abs_action(name='move_base',
parameters=[BQ, BQ2, BT],
conditions=[
And(BaseEq(BQ), SafeManip(BT), BaseMotion(BQ, BQ2, BT))
],
effect=And(BaseEq(BQ2), Not(BaseEq(BQ)))),
#abs_action(name='move_base', parameters=[BQ, BQ2, MQ, BT],
# conditions=[
# And(BaseEq(BQ), TransitManip(MQ), BaseMotion(BQ, BQ2, BT))],
# effect=And(BaseEq(BQ2), Not(BaseEq(BQ)))),
]
# NOTE - the parameters really aren't necessary
# TODO - could remove any dependence on tests because there are so cheap with the evaluation (already can't use them with axioms)
# TODO - could also just make the cost only depend on the introduced objects within the effects
axioms = [
Axiom(effect=Holding(O),
condition=Exists([G], And(GraspEq(O, G), LegalGrasp(O, G)))),
Axiom(effect=InRegion(O, R),
condition=Exists([P], And(PoseEq(O, P), Contained(R, O, P)))),
#Axiom(effect=Safe(O, T), condition=Exists([P], And(PoseEq(O, P), CFree(O, P, T)))),
#Axiom(effect=SafeArm(O, MQ, T), condition=Exists([P, MQ], And(PoseEq(O, P), ManipEq(MQ), CFree(O, P, T)))),
Axiom(effect=SafeManip(BT),
condition=Exists(
[MQ], And(ManipEq(MQ),
TransitManip(MQ)))), # TODO - add condition here
]
# TODO - include parameters in STRIPS axiom?
####################
def get_base_conf(conf):
return Config(base_values_from_full_config(conf.value))
def get_arm_conf(conf):
return Config(
arm_from_full_config(oracle.robot.GetActiveManipulator(),
conf.value))
def sample_poses(o, num_samples=1):
while True:
if AVOID_INITIAL:
oracle.set_all_object_poses(oracle.initial_poses)
else:
oracle.set_all_object_poses({o: oracle.initial_poses[o]})
yield list(
islice(
random_region_placements(oracle,
o,
oracle.get_counters(),
region_weights=True),
num_samples))
def sample_grasps(o):
yield get_grasps(oracle, o)
def sample_region(o, r, num_samples=1):
while True:
oracle.set_all_object_poses({o: oracle.initial_poses[o]})
yield list(
islice(
random_region_placements(oracle,
o, [r],
region_weights=True),
num_samples))
def sample_motion(o, p, g, max_calls=1, max_failures=50):
oracle.set_all_object_poses(
{o: p}) # TODO - saver for the initial state as well?
if oracle.approach_collision(o, p, g):
return
for i in range(max_calls):
pap = PickAndPlace(oracle.get_geom_hash(o), p, g)
if not pap.sample(oracle,
o,
max_failures=max_failures,
sample_vector=False,
sample_arm=False,
check_base=False):
break
#pap.obj = o
yield [(get_base_conf(pap.grasp_config),
get_arm_conf(pap.grasp_config))]
def collision_free(o, p, t):
if p is None or o == t.obj:
return True
holding = ObjGrasp(t.obj, t.grasp)
#holding = Holding(self.oracle.get_body_name(pap.geom_hash), pap.grasp)
if not DO_ARM_MOTION:
return not oracle.holding_collision(t.grasp_config, o, p, holding)
return not oracle.traj_holding_collision(t.approach_config, t.trajs, o,
p, holding)
cond_streams = [
EasyListGenStream(inputs=[O],
outputs=[P],
conditions=[],
effects=[LegalPose(O, P)],
generator=sample_poses),
EasyListGenStream(inputs=[O],
outputs=[G],
conditions=[],
effects=[LegalGrasp(O, G)],
generator=sample_grasps),
EasyListGenStream(inputs=[O, R],
outputs=[P],
conditions=[],
effects=[LegalPose(O, P),
Contained(R, O, P)],
generator=sample_region),
#MultiEasyGenStream(inputs=[O, P, G], outputs=[BQ, MQ], conditions=[LegalPose(O, P), LegalGrasp(O, G)],
# effects=[Kin(O, P, G, BQ, MQ)], generator=sample_motion),
EasyListGenStream(inputs=[O, P, G],
outputs=[BQ, MQ],
conditions=[LegalPose(O, P),
LegalGrasp(O, G)],
effects=[Kin(O, P, G, BQ, MQ),
LegalConf(BQ, MQ)],
generator=sample_motion),
# TODO - this doesn't work for constants
# TODO - make a condition that MQ, MQ2 both work for BQ. Otherwise, it will still create a ton of streams
#EasyGenStream(inputs=[MQ, MQ2, BQ], outputs=[MT], conditions=[],
# effects=[ManipMotion(MQ, MQ2, BQ, MT)], generator=lambda *args: [None], order=1),
EasyGenStream(inputs=[MQ, MQ2, BQ],
outputs=[MT],
conditions=[TransitManip(MQ),
LegalConf(BQ, MQ2)],
effects=[ManipMotion(MQ, MQ2, BQ, MT)],
generator=lambda *args: [None],
order=1),
EasyGenStream(inputs=[MQ, MQ2, BQ],
outputs=[MT],
conditions=[LegalConf(BQ, MQ),
TransitManip(MQ2)],
effects=[ManipMotion(MQ, MQ2, BQ, MT)],
generator=lambda *args: [None],
order=1),
#EasyGenStream(inputs=[MQ, MQ2, BQ], outputs=[MT], conditions=[LegalConf(BQ, MQ), LegalConf(BQ, MQ2)],
# effects=[ManipMotion(MQ, MQ2, BQ, MT)], generator=lambda *args: [None], order=1),
EasyGenStream(inputs=[BQ, BQ2],
outputs=[BT],
conditions=[],
effects=[BaseMotion(BQ, BQ2, BT)],
generator=lambda *args: [None],
order=1),
#effects=[BaseMotion(BQ, BQ2, BT)], generator=lambda *args: [None], order=2), # NOTE - causes a bug!
#EasyTestStream(inputs=[O, P, T], conditions=[LegalPose(O, P)], effects=[CFree(O, P, T)],
# test=collision_free, eager=EAGER_TESTS),
]
####################
#print transit_conf.value
#print oracle.initial_config.value
initial_base = get_base_conf(oracle.initial_config)
initial_manip = get_arm_conf(oracle.initial_config)
print initial_base.value
print initial_manip.value
constants = []
initial_atoms = [
BaseEq(initial_base),
ManipEq(initial_manip),
HandEmpty(),
TransitManip(transit_conf),
LegalConf(initial_base, initial_manip),
]
for obj, pose in oracle.initial_poses.iteritems():
initial_atoms += [PoseEq(obj, pose), LegalPose(obj, pose)]
# TODO - serialize goals by object name
goal_literals = []
if problem.goal_holding is not None:
if problem.goal_holding is False:
goal_literals.append(HandEmpty())
elif isinstance(problem.goal_holding, ObjGrasp):
goal_literals.append(
GraspEq(problem.goal_holding.object_name,
problem.goal_holding.grasp))
elif problem.goal_holding in oracle.get_objects():
goal_literals.append(Holding(problem.goal_holding))
else:
raise Exception()
for obj, pose in problem.goal_poses.iteritems():
goal_literals.append(PoseEq(obj, pose))
initial_atoms.append(LegalPose(obj, pose))
for obj, region in problem.goal_regions.iteritems():
goal_literals.append(InRegion(obj, region))
#goal_formula = And(*goal_literals)
#goal_formula = AbsCondition(map(And, goal_literals)) # TODO - bug where goals must have And
goal_formula = AbsCondition(
goal_literals) # TODO - bug where goals must have And
return STRIPStreamProblem(initial_atoms, goal_formula, actions + axioms,
cond_streams, constants)
def compile_problem(tamp_problem):
O = Param(OBJECT)
O1, O2 = Param(OBJECT), Param(OBJECT)
L = Param(LOCATION)
L_s, L_g = Param(LOCATION), Param(LOCATION) # generic location
Stove_l_s, Stove_l_g = Param(STOVE_L_S), Param(
STOVE_L_G) # locations for stove and sink
Sink_l_s, Sink_l_g = Param(SINK_L_S), Param(SINK_L_G)
actions = [
Action(name='wash',
parameters=[O],
condition=And(InSink(O)),
effect=And(Clean(O))),
Action(name='cook',
parameters=[O],
condition=And(InStove(O), Clean(O)),
effect=And(Cooked(O))),
Action(name='pickplace',
parameters=[O, L_s, L_g],
condition=And(EmptySweptVolume(O, L_s, L_g), AtPose(O, L_s)),
effect=And(AtPose(O, L_g),
Not(AtPose(O, L_s)))) # You should delete!
]
axioms = [
# For all objects in the world, either object is O1 or if not, then it is not in the region
Axiom(effect=EmptySweptVolume(O,L_s,L_g),condition=ForAll([O2],\
Or(Equal(O,O2),\
Exists([L],(And(AtPose(O2,L),OutsideRegion(O,O2,L,L_s,L_g))))))),
Axiom(effect=InStove(O),condition=Exists([L,L_s,L_g],And(AtPose(O,L), Contained(O,L,L_s,L_g), IsStove(L_s,L_g)))),
Axiom(effect=InSink(O),condition=Exists([L,L_s,L_g],And(AtPose(O,L), Contained(O,L,L_s,L_g), IsSink(L_s,L_g)))),
]
cond_streams = [
EasyGenStream(inputs=[O,L_s,L_g], outputs=[L], conditions=[IsSmaller(L_s,L_g)], effects=[Contained(O,L,L_s,L_g)],\
generator=lambda b, ls, lg: (sample_region_pose(b, ls, lg ) for _ in irange(0, INF))),
EasyTestStream(inputs=[L_s,L_g],conditions=[],effects=[IsSmaller(L_s,L_g)],test=is_smaller,eager=EAGER_TESTS),
EasyTestStream(inputs=[L_s,L_g,O,L],conditions=[IsSink(L_s,L_g)],effects=[Contained(O,L,L_s,L_g)],test=in_region,eager=EAGER_TESTS),
EasyTestStream(inputs=[L_s,L_g,O,L],conditions=[IsStove(L_s,L_g)],effects=[Contained(O,L,L_s,L_g)],test=in_region,eager=EAGER_TESTS),
EasyTestStream(inputs=[O,O2,L,L_s,L_g],conditions=[],effects=[OutsideRegion(O,O2,L,L_s,L_g)],test=not_in_region,eager=EAGER_TESTS),
# OutsideRegion tests if the block at L is outside of the region (Ls,Lg)
]
####################
# instantiate the environment region?
constants = [
STOVE_L_S(tamp_problem.stove_region_s),
STOVE_L_G(tamp_problem.stove_region_g),
SINK_L_S(tamp_problem.sink_region_s),
SINK_L_G(tamp_problem.sink_region_g),
]
# define initial state using initial poses of objects
initial_atoms = [
AtPose(block, pose)
for block, pose in tamp_problem.initial_poses.iteritems()
] + [IsSink(tamp_problem.sink_region_s, tamp_problem.sink_region_g)] + [
IsStove(tamp_problem.stove_region_s, tamp_problem.stove_region_g)
]
# static predicate but on steroid
# define goal state as target object to be cooked - can you infer that target object is on Stove
goal_literals = []
# goal_literals.append( AtPose(tamp_problem.blue_obj,1) ) #NOTE: This works; so planner knows how to clear the area
# goal_literals.append( AtPose(tamp_problem.target_obj,3.0) ) #NOTE: But doing this does not work
goal_literals.append(Cooked(tamp_problem.target_obj))
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, constants), static_pred_names
def solve_incrementally():
O = Param(OBJECT)
O1, O2 = Param(OBJECT), Param(OBJECT)
L = Param(LOCATION)
L_s, L_g = Param(LOCATION), Param(LOCATION) # generic location
Stove_l_s, Stove_l_g = Param(STOVE_L_S), Param(
STOVE_L_G) # locations for stove and sink
Sink_l_s, Sink_l_g = Param(SINK_L_S), Param(SINK_L_G)
actions = [
Action(name='wash',
parameters=[O],
condition=And(InSink(O)),
effect=And(Clean(O))),
Action(name='cook',
parameters=[O],
condition=And(InStove(O), Clean(O)),
effect=And(Cooked(O))),
Action(name='pickplace',
parameters=[O, L_s, L_g],
condition=And(EmptySweptVolume(O, L_s, L_g), AtPose(O, L_s)),
effect=And(AtPose(O, L_g),
Not(AtPose(O, L_s)))) # You should delete!
]
axioms = [
# For all objects in the world, either object is O1 or if not, then it is not in the region
Axiom(effect=EmptySweptVolume(O,L_s,L_g),condition=ForAll([O2],\
Or(Equal(O,O2),\
Exists([L],(And(AtPose(O2,L),OutsideRegion(O,O2,L,L_s,L_g))))))),
# Object is in the stove if it is at pose L for which Ls and Lg define stove
Axiom(effect=InStove(O),condition=Exists([L,L_s,L_g],And(AtPose(O,L), Contained(O,L,L_s,L_g), IsStove(L_s,L_g)))),
Axiom(effect=InSink(O),condition=Exists([L,L_s,L_g],And(AtPose(O,L), Contained(O,L,L_s,L_g), IsSink(L_s,L_g)))),
]
cond_streams = [
EasyGenStream(inputs=[O,L_s,L_g], outputs=[L], conditions=[IsSmaller(L_s,L_g)], effects=[Contained(O,L,L_s,L_g)],\
generator=lambda b, ls, lg: (sample_region_pose(b, ls, lg ) for _ in irange(0, INF))),
EasyTestStream(inputs=[L_s,L_g],conditions=[],effects=[IsSmaller(L_s,L_g)],test=is_smaller,eager=EAGER_TESTS),
# Generate static predicates that object is contained in sink for which Ls and Lg define the sink. If L was not continuous value,
# then we would define this in the intial condition and would not be changed by any of the actions (hence static predicate)
EasyTestStream(inputs=[L_s,L_g,O,L],conditions=[IsSink(L_s,L_g)],effects=[Contained(O,L,L_s,L_g)],test=in_region,eager=EAGER_TESTS),
EasyTestStream(inputs=[L_s,L_g,O,L],conditions=[IsStove(L_s,L_g)],effects=[Contained(O,L,L_s,L_g)],test=in_region,eager=EAGER_TESTS),
# OutsideRegion tests if O2 is is outside of the region (Ls,Lg)
EasyTestStream(inputs=[O,O2,L,L_s,L_g],conditions=[],effects=[OutsideRegion(O,O2,L,L_s,L_g)],test=not_in_region,eager=EAGER_TESTS),
]
####################
tamp_problem = sample_one_d_kitchen_problem()
# instantiate the environment region?
constants = [
STOVE_L_S(tamp_problem.stove_region_s),
STOVE_L_G(tamp_problem.stove_region_g),
SINK_L_S(tamp_problem.sink_region_s),
SINK_L_G(tamp_problem.sink_region_g),
]
# define initial state using initial poses of objects
initial_atoms = [
AtPose(block, pose)
for block, pose in tamp_problem.initial_poses.iteritems()
] + [IsSink(tamp_problem.sink_region_s, tamp_problem.sink_region_g)] + [
IsStove(tamp_problem.stove_region_s, tamp_problem.stove_region_g)
] # initial_atoms = static predicates, but on steroid
goal_literals = []
subgoal_list = [ InSink(tamp_problem.target_obj), \
InStove(tamp_problem.target_obj), Cooked(tamp_problem.target_obj) ]
for i in range(len(subgoal_list)):
goal_literals = [subgoal_list[0]]
stream_problem = STRIPStreamProblem(initial_atoms, goal_literals, \
actions+axioms, cond_streams, constants)
search = get_fast_downward('eager')
plan, universe = incremental_planner(stream_problem, search=search,\
frequency=1, verbose=True, max_time=200)
plan = convert_plan(plan)
# move the object to the new locations; todo: add new predicates
if len(plan) > 0: # if no action needed, keep last initial atoms
initial_atoms = []
for action in plan:
action_name = action[0].name
action_args = action[1]
if action_name == 'pickplace':
O = action_args[0].name
pick_l = action_args[1]
place_l = action_args[2]
block = [b for b in tamp_problem.blocks if b.name == O][0]
initial_atoms += [AtPose(block, place_l)]
if len(initial_atoms) == 1:
block = [b for b in tamp_problem.blocks if b.name != O][0]
initial_atoms += [AtPose(block, tamp_problem.initial_poses[block])]
initial_atoms += [
IsSink(tamp_problem.sink_region_s, tamp_problem.sink_region_g)
]
initial_atoms += [
IsStove(tamp_problem.stove_region_s, tamp_problem.stove_region_g)
]
def amber_stripstream(use_window=True, step=False):
problem = dantam2()
window_3d = Window3D(title='World',
windowWidth=500,
noWindow=(not use_window),
viewport=problem.workspace.flatten('F'))
if is_window_active(window_3d):
draw_window_3d(
window_3d, problem.initial_conf,
[body.applyTrans(pose) for body, pose in problem.initial_poses])
raw_input('Start?')
robot = problem.initial_conf.robot
name_to_body = {get_name(body): body for body, _ in problem.initial_poses}
static_bodies = [
body.applyTrans(pose) for body, pose in problem.initial_poses
if get_name(body) not in problem.movable_names
]
block = name_to_body[problem.movable_names[-1]] # Generic object
####################
# NOTE - can either modify the world state or create new bodies
# Collision free test between an object at pose1 and an object at pose2
def cfree_pose(pose1, pose2):
return not ((pose1 == pose2) or block.applyTrans(pose1).collides(
block.applyTrans(pose2)))
def cfree_traj(
traj, pose
): # Collision free test between a robot executing traj (which may or may not involve a grasp) and an object at pose
if DISABLE_TRAJ_COLLISIONS:
return True
placed = [body.applyTrans(pose)]
attached = None
if traj.grasp is not None:
attached = {
ARM:
body.applyTrans(
get_wrist_frame(traj.grasp, robot, ARM).inverse())
}
# TODO - cache bodies for these configurations
collision_fn = get_collision_fn(placed, attached)
return not any(collision_fn(q) for q in traj.path)
####################
# Sample pregrasp config and motion plan that performs a grasp
def sample_grasp_traj(pose, grasp):
attached = {
ARM: body.applyTrans(get_wrist_frame(grasp, robot, ARM).inverse())
}
collision_fn = get_collision_fn(static_bodies, attached)
manip_frame = manip_from_pose_grasp(pose, grasp, robot, ARM)
grasp_conf = next(
inverse_kinematics(problem.initial_conf, manip_frame, ARM),
None) # Grasp configuration
if grasp_conf is None or collision_fn(grasp_conf):
return
# NOTE - I could also do this in the opposite order
pregrasp_frame = approach_from_manip(manip_frame)
pregrasp_conf = next(
inverse_kinematics(grasp_conf, pregrasp_frame,
ARM)) # Pregrasp configuration
if pregrasp_conf is None or collision_fn(pregrasp_conf):
return
if DISABLE_GRASP_TRAJECTORIES:
yield (pregrasp_conf, Traj([grasp_conf], grasp))
return
grasp_path = direct_path(
grasp_conf,
pregrasp_conf, # Trajectory from grasp configuration to pregrasp
get_extend_fn([robot.armChainNames[ARM]], STEP_SIZE),
collision_fn)
if grasp_path is None:
return
yield (pregrasp_conf, Traj(grasp_path, grasp))
def sample_free_motion(conf1, conf2): # Sample motion while not holding
if DISABLE_MOTION_TRAJECTORIES:
yield (Traj([conf2]), )
return
active_chains = [robot.armChainNames[ARM]]
path = birrt(conf1,
conf2,
get_distance_fn(active_chains),
get_sample_fn(conf1, active_chains),
get_extend_fn(active_chains, STEP_SIZE),
get_collision_fn(static_bodies),
restarts=0,
iterations=50,
smooth=None)
if path is None:
return
yield (Traj(path), )
def sample_holding_motion(conf1, conf2,
grasp): # Sample motion while holding
if DISABLE_MOTION_TRAJECTORIES:
yield (Traj([conf2], grasp), )
return
active_chains = [robot.armChainNames[ARM]]
attached = {
ARM: body.applyTrans(get_wrist_frame(grasp, robot, ARM).inverse())
}
path = birrt(conf1,
conf2,
get_distance_fn(active_chains),
get_sample_fn(conf1, active_chains),
get_extend_fn(active_chains, STEP_SIZE),
get_collision_fn(static_bodies, attached),
restarts=0,
iterations=50,
smooth=None)
if path is None:
return
yield (Traj(path, grasp), )
####################
cond_streams = [
# Pick/place trajectory
EasyGenStream(inputs=[P, G],
outputs=[Q, T],
conditions=[],
effects=[GraspMotion(P, G, Q, T)],
generator=sample_grasp_traj),
# Move trajectory
EasyGenStream(inputs=[Q, Q2],
outputs=[T],
conditions=[],
effects=[FreeMotion(Q, Q2, T)],
generator=sample_free_motion,
order=1,
max_level=0),
EasyGenStream(inputs=[Q, Q2, G],
outputs=[T],
conditions=[],
effects=[HoldingMotion(Q, Q2, G, T)],
generator=sample_holding_motion,
order=1,
max_level=0),
# Collisions
EasyTestStream(inputs=[P, P2],
conditions=[],
effects=[CFreePose(P, P2)],
test=cfree_pose,
eager=True),
EasyTestStream(inputs=[T, P],
conditions=[],
effects=[CFreeTraj(T, P)],
test=cfree_traj),
]
####################
constants = map(GRASP, TOP_GRASPS[:MAX_GRASPS]) + map(
POSE, problem.known_poses)
initial_atoms = [
ConfEq(problem.initial_conf),
HandEmpty(),
] + [
PoseEq(get_name(body), pose) for body, pose in problem.initial_poses
if get_name(body) in problem.movable_names
]
goal_literals = [
PoseEq(obj, pose) for obj, pose in problem.goal_poses.iteritems()
]
if problem.goal_conf is not None:
goal_literals.append(ConfEq(problem.goal_conf))
stream_problem = STRIPStreamProblem(initial_atoms, And(*goal_literals),
actions + axioms, cond_streams,
constants)
search_fn = get_fast_downward('eager', max_time=10, verbose=False)
plan, _ = incremental_planner(stream_problem,
search=search_fn,
frequency=1,
waves=True,
debug=False)
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i + 1, action
else:
print 'Failure'
if is_window_active(window_3d) and plan is not None:
robot_conf = problem.initial_conf
placements = {
get_name(body): body.applyTrans(pose)
for body, pose in problem.initial_poses
}
attached = {'left': None, 'right': None}
def _execute_traj(path):
for j, conf in enumerate(path):
draw_window_3d(window_3d, conf, placements.values(), attached)
if step:
raw_input('%s/%s) Step?' % (j, len(path)))
else:
window_3d.update()
sleep(0.01)
draw_window_3d(window_3d, robot_conf, placements.values())
raw_input('Start?')
for i, (action, args) in enumerate(plan):
if action.name == 'move':
_, _, traj = args
_execute_traj(traj.path)
elif action.name == 'move_holding':
_, _, traj, _, _ = args
_execute_traj(traj.path)
elif action.name == 'pick':
obj, _, grasp, _, traj = args
_execute_traj(traj.path[::-1])
attached[ARM] = name_to_body[obj].applyTrans(
get_wrist_frame(grasp, robot, ARM).inverse())
del placements[obj]
_execute_traj(traj.path)
elif action.name == 'place':
obj, pose, _, _, traj = args
_execute_traj(traj.path[::-1])
placements[obj] = name_to_body[obj].applyTrans(pose)
attached[ARM] = None
_execute_traj(traj.path)
else:
raise ValueError(action.name)
raw_input('Finish?')
def compile_problem(tamp_problem):
"""
Constructs a STRIPStream problem for the continuous TMP problem.
:param tamp_problem: a :class:`.TMPProblem`
:return: a :class:`.STRIPStreamProblem`
"""
B1, B2 = Param(BLOCK), Param(BLOCK)
P1, P2 = Param(POSE), Param(POSE)
Q1, Q2 = Param(CONF), Param(CONF)
R = Param(REGION)
actions = [
Action(name='pick',
parameters=[B1, P1, Q1],
condition=And(AtPose(B1, P1), HandEmpty(), AtConf(Q1),
LegalKin(P1, Q1)),
effect=And(Holding(B1), Not(AtPose(B1, P1)), Not(HandEmpty()))),
Action(name='place',
parameters=[B1, P1, Q1],
condition=And(Holding(B1), AtConf(Q1), LegalKin(P1, Q1),
ForAll([B2], Or(Equal(B1, B2), Safe(B2, B1,
P1)))),
effect=And(AtPose(B1, P1), HandEmpty(), Not(Holding(B1)))),
Action(name='move',
parameters=[Q1, Q2],
condition=AtConf(Q1),
effect=And(AtConf(Q2), Not(AtConf(Q1)))),
Action(name='clean',
parameters=[B1, R],
condition=And(InRegion(B1, R), IsSink(R)),
effect=And(Cleaned(B1))),
Action(name='cook',
parameters=[B1, R],
condition=And(InRegion(B1, R), IsStove(R)),
effect=And(Cooked(B1))),
]
axioms = [
Axiom(effect=InRegion(B1, R),
condition=Exists([P1], And(AtPose(B1, P1), Contained(B1, P1,
R)))),
Axiom(effect=Safe(B2, B1, P1),
condition=Exists([P2],
And(AtPose(B2, P2),
CollisionFree(B1, P1, B2, P2)))),
]
cond_streams = [
EasyGenStream(inputs=[R, B1],
outputs=[P1],
conditions=[CanPlace(B1, R)],
effects=[Contained(B1, P1, R)],
generator=lambda r, b:
(sample_region_pose(r, b) for _ in irange(0, INF))),
EasyGenStream(inputs=[P1],
outputs=[Q1],
conditions=[],
effects=[LegalKin(P1, Q1)],
generator=lambda p: iter([inverse_kinematics(p)])),
EasyTestStream(inputs=[R, B1, P1],
conditions=[],
effects=[Contained(B1, P1, R)],
test=in_region,
eager=EAGER_TESTS,
plannable=False),
EasyTestStream(inputs=[B1, P1, B2, P2],
conditions=[],
effects=[CollisionFree(B1, P1, B2, P2)],
test=lambda *args: not are_colliding(*args),
eager=EAGER_TESTS),
]
constants = [
REGION(tamp_problem.env_region),
]
initial_atoms = [
AtConf(tamp_problem.initial_config),
] + [
AtPose(block, pose)
for block, pose in tamp_problem.initial_poses.iteritems()
] + [
CanPlace(block, tamp_problem.env_region)
for block in tamp_problem.initial_poses
]
if tamp_problem.initial_holding is None:
initial_atoms.append(HandEmpty())
else:
initial_atoms.append(Holding(tamp_problem.initial_holding))
goal_literals = []
if tamp_problem.goal_config is not None:
goal_literals.append(AtConf(tamp_problem.goal_config))
if tamp_problem.goal_holding is False:
goal_literals.append(HandEmpty())
elif tamp_problem.goal_holding is not None:
goal_literals.append(Holding(tamp_problem.goal_holding))
for block, goal in tamp_problem.goal_poses:
goal_literals.append(AtPose(block, goal))
for block, goal in tamp_problem.goal_regions:
initial_atoms.append(CanPlace(block, goal))
goal_literals.append(InRegion(block, goal))
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, constants)
def compile_problem(tamp_problem):
"""
Constructs a STRIPStream problem for the countable TMP problem.
:param tamp_problem: a :class:`.TMPProblem`
:return: a :class:`.STRIPStreamProblem`
"""
# NOTE - the simple focused algorithm gives "Could not find instantiation for PNE!" when this is moved outside
B1, B2 = Param(BLOCK), Param(BLOCK)
P1, P2 = Param(POSE), Param(POSE)
Q1, Q2 = Param(CONF), Param(CONF)
actions = [
Action(name='pick',
parameters=[B1, P1, Q1],
condition=And(AtPose(B1, P1), HandEmpty(), AtConf(Q1),
LegalKin(P1, Q1)),
effect=And(Holding(B1), Not(AtPose(B1, P1)), Not(HandEmpty()))),
Action(
name='place',
parameters=[B1, P1, Q1],
condition=And(
Holding(B1),
AtConf(Q1),
LegalKin(P1, Q1),
#ForAll([B2], Or(Equal(B1, B2), Safe(B2, B1, P1)))),
ForAll([B2], Or(Equal(B1, B2), Safe(B2, P1)))),
#*[Or(Equal(B1, BLOCK(b2)), Safe(b2, P1)) for b2 in tamp_problem.initial_poses]),
effect=And(AtPose(B1, P1), HandEmpty(), Not(Holding(B1)))),
Action(name='move',
parameters=[Q1, Q2],
condition=AtConf(Q1),
effect=And(AtConf(Q2), Not(AtConf(Q1)))),
]
axioms = [
#Axiom(effect=Safe(B2, B1, P1), condition=Exists([P2], And(AtPose(B2, P2), CollisionFree(B1, P1, B2, P2)))),
Axiom(effect=Safe(B2, P1),
condition=Exists([P2], And(AtPose(B2, P2), CollisionFree(P1,
P2)))),
]
# NOTE - this needs to be inside the method so you make new streams each time
cond_streams = [
#EasyGenStream(inputs=[P2], outputs=[P1], conditions=[], effects=[],
# generator=lambda a: irange(0, NUM_POSES)),
EasyGenStream(inputs=[],
outputs=[P1],
conditions=[],
effects=[],
generator=lambda: irange(0, NUM_POSES)),
EasyGenStream(inputs=[P1],
outputs=[Q1],
conditions=[],
effects=[LegalKin(P1, Q1)],
generator=lambda p: iter([p])),
#EasyTestStream(inputs=[B1, P1, B2, P2], conditions=[], effects=[CollisionFree(B1, P1, B2, P2)],
# test=lambda b1, p1, b2, p2: p1 != p2, eager=EAGER_TESTS),
EasyTestStream(inputs=[P1, P2],
conditions=[],
effects=[CollisionFree(P1, P2)],
test=lambda p1, p2: p1 != p2,
eager=EAGER_TESTS),
]
constants = []
initial_atoms = [
AtConf(tamp_problem.initial_config),
] + [
AtPose(block, pose)
for block, pose in tamp_problem.initial_poses.iteritems()
]
if tamp_problem.initial_holding is False:
initial_atoms.append(HandEmpty())
else:
initial_atoms.append(Holding(tamp_problem.initial_holding, BLOCK))
goal_literals = []
if tamp_problem.goal_holding is False:
goal_literals.append(HandEmpty())
elif tamp_problem.goal_holding is not None:
goal_literals.append(Holding(tamp_problem.goal_holding))
for block, goal in tamp_problem.goal_poses.iteritems():
goal_literals.append(AtPose(block, goal))
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, constants)