def create_problem():
"""
Creates a blocksworld STRIPStream problem.
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
BLOCK = Type()
# Fluent predicates
Clear = Pred(BLOCK)
OnTable = Pred(BLOCK)
ArmEmpty = Pred()
Holding = Pred(BLOCK)
On = Pred(BLOCK, BLOCK)
# Free parameters
B1, B2 = Param(BLOCK), Param(BLOCK)
rename_easy(locals())
actions = [
Action(name='pickup', parameters=[B1],
condition=And(Clear(B1), OnTable(B1), ArmEmpty()),
effect=And(Holding(B1), Not(Clear(B1)), Not(OnTable(B1)), Not(ArmEmpty()))),
Action(name='putdown', parameters=[B1],
condition=Holding(B1),
effect=And(Clear(B1), OnTable(B1), ArmEmpty(), Not(Holding(B1)))),
Action(name='stack', parameters=[B1, B2],
condition=And(Clear(B2), Holding(B1)),
effect=And(Clear(B1), On(B1, B2), ArmEmpty(), Not(Clear(B2)), Not(Holding(B1)))),
Action(name='unstack', parameters=[B1, B2],
condition=And(Clear(B1), On(B1, B2), ArmEmpty()),
effect=And(Clear(B2), Holding(B1), Not(Clear(B1)), Not(On(B1, B2)), Not(ArmEmpty()))),
]
axioms = []
cond_streams = []
constants = []
initial_atoms = [
OnTable('A'),
OnTable('B'),
OnTable('C'),
Clear('A'),
Clear('B'),
Clear('C'),
ArmEmpty(),
]
goal_literals = [On('A', 'B'), On('B', 'C')]
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms, cond_streams, constants)
def __init__(self):
block, pose, config = P('o', BLOCK), P('p', POSE), P('q', CONFIG)
STRIPSAction.__init__(self, self.__class__.__name__,
[block, pose, config], [
AtPose(block, pose),
HandEmpty(),
IsIK(pose, config),
], [
Holding(block),
Not(HandEmpty()),
Not(AtPose(block, pose)),
])
Refinable.__init__(self, [H0Pick1(block, pose, config)])
def __init__(self,
block=P('o', BLOCK),
pose=P('p', POSE),
config=P('q', CONFIG)):
#def __init__(self, block, pose, config):
super(H0Pick1, self).__init__(self.__class__.__name__,
[block, pose, config], [
AtPose(block, pose),
HandEmpty(),
IsIK(pose, config),
AtConfig(config),
], [
Holding(block),
Not(HandEmpty()),
Not(AtPose(block, pose)),
])
def __init__(self):
params = (P('o', BLOCK), P('p', POSE), P('q', CONFIG))
block, pose, config = params
conditions = [
AtPose(block, pose),
HandEmpty(),
IsIK(pose, config),
#IsContained(block, pose, ENV_REGION),
]
if USE_BASE:
conditions.append(AtConfig(config))
super(Pick, self).__init__(self.__class__.__name__, params, conditions,
[
Holding(block),
Not(HandEmpty()),
Not(AtPose(block, pose)),
])
def __init__(self):
super(Land, self).__init__(self.__class__.__name__, [], [
Flying(),
AtState(State((0, 0))),
], [
Landed(),
Not(Flying()),
])
def get_effects(var_map, effect_vars, assign_map):
effects = []
for var in effect_vars:
name, args = var_name(var), make_var_constants(var, var_map)
pre_args = [assign_map.get(p, p) for p in args + [X[var]]]
eff_args = [assign_map.get(p, p) for p in args + [nX[var]]]
predicate = var_map[name].predicate
effects += [predicate(*eff_args), Not(predicate(*pre_args))]
return effects
def create_problem(time_step=.5, fuel_rate=5, start_fuel=10, goal_p=10):
"""
Creates the Tsiolkovsky rocket problem.
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
STATE, RATE, MASS, TIME = Type(), Type(), Type(), Type()
HEIGHT = Type()
# Fluent predicates
AtState = Pred(STATE)
# Derived predicates
Above = Pred(HEIGHT)
# Static predicates
IsBurst = Pred(STATE, RATE, TIME, STATE)
IsAbove = Pred(STATE, HEIGHT)
# Free parameters
X1, X2 = Param(STATE), Param(STATE)
Q, T = Param(RATE), Param(TIME)
H = Param(HEIGHT)
rename_easy(locals()) # Trick to make debugging easier
####################
actions = [
Action(name='burst',
parameters=[X1, Q, T, X2],
condition=And(AtState(X1), IsBurst(X1, Q, T, X2)),
effect=And(AtState(X2), Not(AtState(X1)))),
]
axioms = [
Axiom(effect=Above(H),
condition=Exists([X1], And(AtState(X1), IsAbove(X1, H)))),
]
####################
# Conditional stream declarations
cond_streams = [
GeneratorStream(inputs=[X1, Q, T],
outputs=[X2],
conditions=[],
effects=[IsBurst(X1, Q, T, X2)],
generator=forward_burst),
TestStream(inputs=[X1, H],
conditions=[],
effects=[IsAbove(X1, H)],
test=lambda (p, v, m), h: p >= h,
eager=True),
]
def __init__(self):
params = (P('q1', CONFIG), P('q2', CONFIG))
q1, q2 = params
super(Move, self).__init__(self.__class__.__name__, params, [
AtConfig(q1),
], [
AtConfig(q2),
Not(AtConfig(q1)),
])
def __init__(self):
obj, p, prior = Param(OBJ), Param(POSE), Param(BELIEF)
post = 1
super(PerfectLook, self).__init__(
self.__class__.__name__,
[obj, p, prior],
And(
#At(obj, p),
BAt(obj, p, prior)),
And(BAt(obj, p, 1), Not(BAt(obj, p, prior))))
def create_abstract_problem(state, goal):
# Goal serialization is basically like this
# Necessary and sufficient?
tables, initial_poses, initial_config, holding = state
# TODO: connect this to goals on the actual states
# The quantification of poses and grasps is independent
# Conditions are derived or atoms
actions = [
Action(
name='pick',
parameters=[B, S],
condition=And(On(B, S), HandEmpty()), #, AtConf(Q)),
#condition=And(On(B, S), HandEmpty(), Not(Holding(B))), # , AtConf(Q)),
effect=And(Holding(B), Not(On(B, S)), Not(HandEmpty()))),
Action(
name='place',
parameters=[B, S],
condition=And(Holding(B)), #, AtConf(Q)),
effect=And(On(B, S), HandEmpty(), Not(Holding(B)))),
#Action(name='move', parameters=[S1, Q2],
# condition=AtConf(Q),
# effect=And(AtConf(Q2), Not(AtConf(Q)))),
]
axioms = []
cond_streams = []
initial_atoms = [
#AtConf(initial_config),
HandEmpty()
] + [On(block, pose.sur) for block, pose in initial_poses.items()]
goal_literals = [On(b, s) for b, s in goal.on.items()]
if goal.holding is not None:
goal_literals.append(Holding(goal.holding))
problem = STRIPStreamProblem(initial_atoms, goal_literals,
actions + axioms, cond_streams)
return problem
def __init__(self):
params = (P('x', STATE), P('p', POSITION), P('s', SATELLITE))
x, p, s = params
super(Deploy, self).__init__(self.__class__.__name__, params, [
Carrying(s),
AtState(x),
ArePair(x, p),
], [
AtOrbit(s, p),
Not(Carrying(s)),
])
def __init__(self):
params = (P('x1', STATE), P('t', TIME), P('x2', STATE))
x1, t, x2 = params
super(Glide, self).__init__(self.__class__.__name__, params, [
AtState(x1),
IsGlide(x1, t, x2),
Flying(),
], [
AtState(x2),
Not(AtState(x1)),
])
def __init__(self, p_obs_t):
obj, p, prior, post = Param(OBJ), Param(POSE), Param(BELIEF), Param(
BELIEF)
super(BackwardLook, self).__init__(
self.__class__.__name__,
[obj, p, prior, post],
And(
#At(obj, p),
BAt(obj, p, prior),
#BAtAbove(obj, p, prior), # Problem where we want to delete the old belief, but we need to include the reference belief
IsPossible(prior, post)),
And(BAt(obj, p, post), Not(BAt(obj, p, prior))))
def __init__(self, oracle):
params = (P('b', BLOCK), P('p', POSE), P('g', GRASP), P('q', CONFIG),
P('t', TRAJ))
b, p, g, q, t = params
conditions = [
AtPose(b, p),
HandEmpty(),
IsPose(b, p), # NOTE - might not need these
IsGrasp(b, g),
IsIK(b, p, g, q, t),
]
if BASE:
conditions.append(AtConfig(q))
conditions += collision_conditions(oracle, b, t)
effects = [
#HasGrasp(b, g),
Holding(b),
HasGrasp(g),
Not(HandEmpty()),
Not(AtPose(b, p)),
]
super(Pick, self).__init__(self.__class__.__name__, params, conditions,
effects)
def __init__(self, oracle):
params = (P('q1', CONFIG), P('q2', CONFIG))
q1, q2 = params
super(Move, self).__init__(
self.__class__.__name__,
params,
[
AtConfig(q1),
],
[
AtConfig(q2),
Not(AtConfig(q1)),
#Cost(???), # TODO - include cost lowerbound first
#Cost(???), # NOTE - PDDL uses the last cost specified
])
def __init__(self, min_safe_p=.95):
obj, p1, p2, b = Param(OBJ), Param(POSE), Param(POSE), Param(BELIEF)
Action.__init__(
self,
self.__class__.__name__,
[obj, p1, p2, b],
And(
#At(obj, p1),
BAt(obj, p1, b),
BAtAbove(obj, p1, min_safe_p)),
And(
#At(obj, p2),
BAt(obj, p2, b),
#Not(At(obj, p1)),
Not(BAt(obj, p1, b))),
cost=1)
def __init__(self, min_safe_p=.95):
obj, d1, d2 = Param(OBJ), Param(DIST), Param(DIST)
Action.__init__(
self,
self.__class__.__name__,
[obj, d],
And(
BClean(obj, d1),
IsClean(d1, d2),
#BAtAbove(obj, p1, min_safe_p)
),
And(
BClean(obj, d2),
#Not(At(obj, p1)),
Not(BClean(obj, d1))),
cost=1)
def __init__(self):
params = (P('x1',
STATE), P('a', ACCELERATION), P('t', TIME), P('x2', STATE))
x1, a, t, x2 = params
super(Burst, self).__init__(
self.__class__.__name__,
params,
[
AtState(x1),
IsBurst(x1, a, t, x2),
Flying(),
],
[
#Cost(), # Delta fuel
AtState(x2),
Not(AtState(x1)),
])
def __init__(self, oracle):
params = (P('q1', CONF), P('q2', CONF))
q1, q2 = params
super(Move, self).__init__(
self.__class__.__name__,
params,
[
AtConfig(q1),
#MoveCost(q1, q2),
#Initialize(MoveCost(q1, q2)), # TODO - should I define this here?
IsCollisionFree(q1, q2),
],
[
AtConfig(q2),
Not(AtConfig(q1)),
Cost(MoveCost(q1, q2)),
])
def __init__(self, blocks):
params = (P('o', BLOCK), P('p', POSE), P('q', CONFIG))
block, pose, config = params
conditions = [
Holding(block),
IsIK(pose, config), # IsPose(p), IsConfig(q),
]
if COLLISIONS:
conditions += [Safe(other, pose) for other in blocks
] # TODO - always self safe using equality
if BASE:
conditions.append(AtConfig(config))
super(Place, self).__init__(self.__class__.__name__, params,
conditions, [
AtPose(block, pose),
HandEmpty(),
Not(Holding(block)),
])
def __init__(self, p_obs_t, p_obs_f=None, log_cost=False):
#self.p_obs_t = p_obs_t
#if p_obs_f is None: self.p_obs_f = 1 - p_obs_t
#self.log_cost = log_cost
cost = prob_cost(
p_obs_t, log_cost=log_cost) # Conditions on successful observation
cost = None
obj, p, prior, post = Param(OBJ), Param(POSE), Param(BELIEF), Param(
BELIEF)
super(Look, self).__init__(
self.__class__.__name__,
[obj, p, prior, post],
And(
#At(obj, p),
BAt(obj, p, prior),
IsUpdate(prior, post)),
And(BAt(obj, p, post), Not(BAt(obj, p, prior))),
cost)
O, P, G = Param(OBJ), Param(POSE), Param(GRASP)
O2, P2 = Param(OBJ), Param(POSE)
Q, Q2 = Param(CONF), Param(CONF)
T = Param(TRAJ)
rename_easy(locals())
####################
actions = [
Action(name='pick', parameters=[O, P, G, Q, T],
condition=And(PoseEq(O, P), HandEmpty(), ConfEq(Q), GraspMotion(P, G, Q, T),
ForAll([O2], Or(Equal(O, O2), SafeTraj(O2, T)))),
#ForAll([O2], Or(Equal(O, O2), And(SafePose(O2, P), SafeGTraj(O2, GT))))),
effect=And(GraspEq(O, G), Not(HandEmpty()), Not(PoseEq(O, P)))),
Action(name='place', parameters=[O, P, G, Q, T],
condition=And(GraspEq(O, G), ConfEq(Q), GraspMotion(P, G, Q, T),
ForAll([O2], Or(Equal(O, O2), And(SafePose(O2, P), SafeTraj(O2, T))))),
effect=And(PoseEq(O, P), HandEmpty(), Not(GraspEq(O, G)))),
Action(name='move', parameters=[Q, Q2, T],
condition=And(ConfEq(Q), HandEmpty(), FreeMotion(Q, Q2, T),
ForAll([O2], SafeTraj(O2, T))),
effect=And(ConfEq(Q2), Not(ConfEq(Q)))),
Action(name='move_holding', parameters=[Q, Q2, T, O, G],
condition=And(ConfEq(Q), GraspEq(O, G), HoldingMotion(Q, Q2, G, T),
ForAll([O2], Or(Equal(O, O2), SafeTraj(O2, T)))),
effect=And(ConfEq(Q2), Not(ConfEq(Q)))),
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 create_problem(dt, GoalTest, ClearTest, CalcDiffSystem, InitialState):
"""
Creates a generic non-holonomic motion planning problem
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
X, DX, U = Type(), Type(), Type()
# Fluent predicates
AtX = Pred(X)
# Fluent predicates
GoalReached = Pred()
# Static predicates
ComputeDX = Pred(X, U, DX)
Dynamics = Pred(X, DX, X)
AtGoal = Pred(X)
Clear = Pred(X, X)
# Free parameters
X1, X2 = Param(X), Param(X)
DX1 = Param(DX)
U1 = Param(U)
rename_easy(locals()) # Trick to make debugging easier
####################
actions = [
Action(name='simulate',
parameters=[X1, U1, X2, DX1],
condition=And(AtX(X1), ComputeDX(X1, U1, DX1),
Dynamics(X1, DX1, X2), Clear(X1, X2)),
effect=And(Not(AtX(X1)), AtX(X2))),
]
axioms = [
Axiom(effect=GoalReached(), condition=Exists([X1], AtGoal(X1))),
]
####################
# Conditional stream declarations
def ComputeDXCalculator(X1F, DX1F):
X2F = X1F[:]
for i in range(len(X2F)):
X2F[i] = X1F[i] + dt * DX1F[i]
return X2F
cond_streams = [
FunctionStream(inputs=[X1, DX1],
outputs=[X2],
conditions=[],
effects=[Dynamics(X1, DX1, X2)],
function=ComputeDXCalculator),
FunctionStream(inputs=[X1, U1],
outputs=[DX1],
conditions=[],
effects=[ComputeDX(X1, U1, DX1)],
function=CalcDiffSystem),
TestStream(inputs=[X1, X2],
conditions=[],
effects=[Clear(X1, X2)],
test=ClearTest,
eager=True),
TestStream(inputs=[X1],
conditions=[],
effects=[AtGoal(X1)],
test=GoalTest,
eager=True),
]
####################
constants = [
# TODO - need to declare control inputs (or make a stream for them)
]
initial_atoms = [
AtX(InitialState),
]
goal_literals = [GoalReached()]
problem = STRIPStreamProblem(initial_atoms, goal_literals,
actions + axioms, cond_streams, constants)
return problem
def create_problem(goal, obstacles=(), distance=.25, digits=3):
"""
Creates a Probabilistic Roadmap (PRM) motion planning problem.
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
POINT = Type()
REGION = Type()
# Fluent predicates
AtPoint = Pred(POINT)
# Derived predicates
InRegion = Pred(REGION)
#IsReachable = Pred(POINT, POINT)
IsReachable = Pred(POINT)
# Stream predicates
IsEdge = Pred(POINT, POINT)
Contained = Pred(POINT, REGION)
# Free parameters
P1, P2 = Param(POINT), Param(POINT)
R = Param(REGION)
rename_easy(locals()) # Trick to make debugging easier
####################
actions = [
Action(name='move',
parameters=[P1, P2],
condition=And(AtPoint(P1), IsReachable(P2)),
effect=And(AtPoint(P2), Not(AtPoint(P1))))
]
axioms = [
Axiom(effect=InRegion(R),
condition=Exists([P1], And(AtPoint(P1), Contained(P1, R)))),
Axiom(effect=IsReachable(P2),
condition=Or(AtPoint(P2),
Exists([P1], And(IsReachable(P1), IsEdge(P1,
P2))))),
]
####################
def sampler():
for _ in inf_sequence():
yield [(sample(digits), ) for _ in range(10)]
roadmap = set()
def test(p1, p2):
if not (get_distance(p1, p2) <= distance and is_collision_free(
(p1, p2), obstacles)):
return False
roadmap.add((p1, p2))
return True
####################
# Conditional stream declarations
cond_streams = [
EasyListGenStream(
inputs=[],
outputs=[P1],
conditions=[],
effects=[],
generator=sampler
), # NOTE - version that only generators collision-free points
GeneratorStream(inputs=[R],
outputs=[P1],
conditions=[],
effects=[Contained(P1, R)],
generator=lambda r:
(sample_box(r) for _ in inf_sequence())),
TestStream(inputs=[P1, P2],
conditions=[],
effects=[IsEdge(P1, P2), IsEdge(P2, P1)],
test=test,
eager=True),
]
####################
constants = []
initial_atoms = [
AtPoint((0, 0)),
]
goal_literals = []
if is_region(goal):
goal_literals.append(InRegion(goal))
else:
goal_literals.append(AtPoint(goal))
problem = STRIPStreamProblem(initial_atoms, goal_literals,
actions + axioms, cond_streams, constants)
return problem, roadmap
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 create_problem(initRobotPos = (0.5, 0.5),
initRobotVar = 0.01,
maxMoveDist = 5.0,
beaconPos = (1, 1),
homePos = (0, 0),
goalPosEps = 0.1,
goalVar = 0.1,
odoErrorRate = 0.1,
obsVarPerDistFromSensor = 10.0,
minObsVar = 0.001,
domainSize = 20,
verboseFns = True):
"""
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
POS = Type() # 2D position
VAR = Type() # 2D variance
DIST = Type() # positive scalar
# Fluent predicates
RobotPos = Pred(POS)
RobotVar = Pred(VAR)
# Derived predicates
KnowYouAreHome = Pred()
# Static predicates
DistBetween = Pred(POS, POS, DIST) # Distance between two positions (function)
LessThanV = Pred(VAR, VAR) # Less than, on distances
LessThanD = Pred(DIST, DIST) # Less than, on distances
OdometryVar = Pred(VAR, VAR, DIST) # How does odometry variance increase?
SensorVar = Pred(VAR, VAR, DIST) # How does an observation decrease variance?
LegalPos = Pred(POS) # Any legal robot pos
# Free parameters
RPOS1, RPOS2, RVAR1, RVAR2 = Param(POS), Param(POS), Param(VAR), Param(VAR)
DIST1, DIST2 = Param(DIST), Param(DIST)
def odoVarFun(rv, d):
odoVar = (d * odoErrorRate)**2
result = rv + odoVar
if verboseFns: print 'ovf:', rv, d, result
return [result]
def sensorVarFun(rv, d):
obsVar = max(d / obsVarPerDistFromSensor, minObsVar)
result = 1.0 / ((1.0 / rv) + (1.0 / obsVar))
if verboseFns: print 'svf:', rv, d, result
return [result]
def randPos():
while True:
result = (random.random() * domainSize, random.random() * domainSize)
print 'rp:', result
yield [result]
def legalTest(rp):
(x, y) = rp
result = (0 <= x <= domainSize) and (0 <= y <= domainSize)
if not result: print 'not legal:', rp
return result
actions = [
Action(name='Move',
parameters=[RPOS1, RPOS2, RVAR1, RVAR2, DIST1],
condition = And(RobotPos(RPOS1),
RobotVar(RVAR1),
LegalPos(RPOS2), # Generate an intermediate pos
DistBetween(RPOS1, RPOS2, DIST1),
LessThanD(DIST1, maxMoveDist),
OdometryVar(RVAR1, RVAR2, DIST1)),
effect = And(RobotPos(RPOS2),
RobotVar(RVAR2),
Not(RobotPos(RPOS1)),
Not(RobotVar(RVAR1)))),
# Action(name='Look',
# parameters=[RPOS1, RVAR1, RVAR2, DIST1],
# condition = And(RobotPos(RPOS1),
# RobotVar(RVAR1),
# DistBetween(RPOS1, beaconPos, DIST1),
# SensorVar(RVAR1, RVAR2, DIST1)),
# effect = And(RobotVar(RVAR2),
# Not(RobotVar(RVAR1))))
]
axioms = [
Axiom(effect = KnowYouAreHome(),
condition = Exists([RPOS1, RVAR1, DIST1],
And(RobotPos(RPOS1),
RobotVar(RVAR1),
DistBetween(RPOS1, homePos, DIST1),
LessThanD(DIST1, goalPosEps),
LessThanV(RVAR1, goalVar))))
]
# Conditional stream declarations
cond_streams = [
TestStream(inputs = [RPOS1],
conditions = [],
effects = [LegalPos(RPOS1)],
test = legalTest,
eager = True),
GeneratorStream(inputs = [],
outputs = [RPOS1],
conditions = [],
effects = [LegalPos(RPOS1)],
generator = randPos),
GeneratorStream(inputs = [RPOS1, RPOS2],
outputs = [DIST1],
conditions = [],
effects = [DistBetween(RPOS1, RPOS2, DIST1)],
generator = lambda rp1, rp2: [distance(rp1, rp2)]),
GeneratorStream(inputs = [RVAR1, DIST1],
outputs = [RVAR2],
conditions = [],
effects = [OdometryVar(RVAR1, RVAR2, DIST1)],
generator = odoVarFun),
# GeneratorStream(inputs = [RVAR1, DIST1],
# outputs = [RVAR2],
# conditions = [],
# effects = [SensorVar(RVAR1, RVAR2, DIST1)],
# generator = sensorVarFun),
TestStream(inputs = [DIST1, DIST2],
conditions = [],
effects = [LessThanD(DIST1, DIST2)],
test = lt,
eager = True),
TestStream(inputs = [RVAR1, RVAR2],
conditions = [],
effects = [LessThanV(RVAR1, RVAR2)],
test = lt, eager = True)
]
####################
constants = [
]
initial_atoms = [
RobotPos(initRobotPos),
RobotVar(initRobotVar),
LegalPos(homePos),
LegalPos((.1, .1)),
LegalPos((3.0, .1)),
LegalPos((6.0, .1))
]
goal_literals = [
KnowYouAreHome()
]
problem = STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, constants)
return problem
def add_refined(self):
self.add_conditions(Not(self.RefinedPredicate(*self.parameters)))
for operator in self.refinement:
operator.add_conditions(
self.get_valid_atom(operator, self.parameters))
def create_problem(goal, obstacles=(), distance=.25, digits=3):
"""
Creates a Probabilistic Roadmap (PRM) motion planning problem.
:return: a :class:`.STRIPStreamProblem`
"""
# Data types
POINT = Type()
REGION = Type()
# Fluent predicates
AtPoint = Pred(POINT)
# Derived predicates
InRegion = Pred(REGION)
# Stream predicates
AreNearby = Pred(POINT, POINT)
IsEdge = Pred(POINT, POINT)
Contained = Pred(POINT, REGION)
# Functions
Distance = Func(POINT, POINT)
# Free parameters
P1, P2 = Param(POINT), Param(POINT)
R = Param(REGION)
rename_easy(locals()) # Trick to make debugging easier
####################
actions = [
#STRIPSAction(name='move', parameters=[P1, P2],
# conditions=[AtPoint(P1), IsEdge(P1, P2)],
# effects=[AtPoint(P2), Not(AtPoint(P1))], cost=1), # Fixed cost
#STRIPSAction(name='move', parameters=[P1, P2],
# conditions=[AtPoint(P1), IsEdge(P1, P2)],
# effects=[AtPoint(P2), Not(AtPoint(P1)), Cost(Distance(P1, P2))]), # Cost depends on parameters
Action(name='move',
parameters=[P1, P2],
condition=And(AtPoint(P1), IsEdge(P1, P2)),
effect=And(AtPoint(P2), Not(AtPoint(P1))),
costs=[1, Distance(P1, P2)]),
]
axioms = [
#Axiom(effect=GoalReached(), condition=Exists([P1], And(AtPos(P1), Contained(P1)))),
STRIPSAxiom(conditions=[AtPoint(P1), Contained(P1, R)],
effects=[InRegion(R)])
]
####################
# Conditional stream declarations
cond_streams = [
GeneratorStream(
inputs=[],
outputs=[P1],
conditions=[],
effects=[],
generator=lambda: ((sample(digits), ) for _ in inf_sequence())
), # NOTE - version that only generators collision-free points
GeneratorStream(inputs=[R],
outputs=[P1],
conditions=[],
effects=[Contained(P1, R)],
generator=lambda r:
((sample_box(r), ) for _ in inf_sequence())),
#TestStream(inputs=[P1, P2], conditions=[], effects=[AreNearby(P1, P2), AreNearby(P2, P1)],
# test=lambda p1, p2: get_distance(p1, p2) <= distance, eager=True),
#TestStream(inputs=[P1, P2], conditions=[AreNearby(P1, P2)], effects=[IsEdge(P1, P2), IsEdge(P2, P1)],
# test=lambda p1, p2: is_collision_free((p1, p2), obstacles), eager=True),
TestStream(
inputs=[P1, P2],
conditions=[],
effects=[IsEdge(P1, P2), IsEdge(P2, P1)],
#test=lambda p1, p2: is_collision_free((p1, p2), obstacles), eager=True),
test=lambda p1, p2:
(get_distance(p1, p2) <= distance) and is_collision_free(
(p1, p2), obstacles),
eager=True),
#TestStream(inputs=[P1, P2], conditions=[], effects=[IsEdge(P1, P2), IsEdge(P2, P1)],
# test=lambda p1, p2: get_distance(p1, p2) <= distance and is_collision_free((p1, p2), obstacles), eager=True),
#TestStream(inputs=[P1, R], conditions=[], effects=[Contained(P1, R)],
# test=lambda p, r: contains(p, r), eager=True),
CostStream(inputs=[P1, P2],
conditions=[],
effects=[Distance(P1, P2),
Distance(P2, P1)],
function=get_distance,
scale=100,
eager=True),
]
####################
constants = []
initial_atoms = [
AtPoint((0, 0)),
]
goal_literals = []
if is_region(goal):
goal_literals.append(InRegion(goal))
else:
goal_literals.append(AtPoint(goal))
problem = STRIPStreamProblem(initial_atoms, goal_literals,
actions + axioms, cond_streams, constants)
return problem
def create_problem():
"""
Creates the 1D task and motion planning STRIPStream problem.
:return: a :class:`.STRIPStreamProblem`
"""
blocks = ['block%i'%i for i in range(3)]
num_poses = pow(10, 10)
initial_config = 0 # the initial robot configuration is 0
initial_poses = {block: i for i, block in enumerate(blocks)} # the initial pose for block i is i
goal_poses = {block: i+1 for i, block in enumerate(blocks)} # the goal pose for block i is i+1
####################
VALUE = Type()
# Fluent predicates
AtConf = Pred(VALUE)
AtPose = Pred(VALUE, VALUE)
HandEmpty = Pred()
Holding = Pred(VALUE)
# Derived predicates
Safe = Pred(VALUE, VALUE, VALUE)
# Static predicates
IsBlock = Pred(VALUE)
IsPose = Pred(VALUE)
IsConf = Pred(VALUE)
LegalKin = Pred(VALUE, VALUE)
CollisionFree = Pred(VALUE, VALUE, VALUE, VALUE)
# Free parameters
B1, B2 = Param(VALUE), Param(VALUE)
P1, P2 = Param(VALUE), Param(VALUE)
Q1, Q2 = Param(VALUE), Param(VALUE)
rename_easy(locals()) # Trick to make debugging easier
####################
# NOTE - it would be easier to just update an in hand pose
actions = [
STRIPSAction(name='pick', parameters=[B1, P1, Q1],
conditions=[IsBlock(B1), IsPose(P1), IsConf(Q1), LegalKin(P1, Q1),
AtPose(B1, P1), HandEmpty(), AtConf(Q1)],
effects=[Holding(B1), Not(AtPose(B1, P1)), Not(HandEmpty())]),
STRIPSAction(name='place', parameters=[B1, P1, Q1],
conditions=[IsBlock(B1), IsPose(P1), IsConf(Q1), LegalKin(P1, Q1),
Holding(B1), AtConf(Q1)] + [Safe(b2, B1, P1) for b2 in blocks],
effects=[AtPose(B1, P1), HandEmpty(), Not(Holding(B1))]),
STRIPSAction(name='move', parameters=[Q1, Q2],
conditions=[IsConf(Q1), IsConf(Q2), AtConf(Q1)],
effects=[AtConf(Q2), Not(AtConf(Q1))]),
]
axioms = [ # TODO - need to combine axioms
#Axiom(effect=Safe(B2, B1, P1),
# condition=Exists([P2], And(AtPose(B2, P2), CollisionFree(B1, P1, B2, P2)))), # Infers B2 is at a safe pose wrt B1 at P1
]
####################
# Conditional stream declarations
cond_streams = [
GeneratorStream(inputs=[], outputs=[P1], conditions=[], effects=[IsPose(P1)],
generator=lambda: xrange(num_poses)), # Enumerating all the poses
GeneratorStream(inputs=[P1], outputs=[Q1], conditions=[IsPose(P1)], effects=[IsConf(Q1), LegalKin(P1, Q1)],
generator=lambda p: [p]), # Inverse kinematics
TestStream(inputs=[B1, P1, B2, P2], conditions=[IsBlock(B1), IsPose(P1), IsBlock(B2), IsPose(P2)],
effects=[CollisionFree(B1, P1, B2, P2)], test=lambda b1, p1, b2, p2: p1 != p2, eager=True), # Collision checking
]
####################
constants = []
initial_atoms = [
IsConf(initial_config),
AtConf(initial_config),
HandEmpty()
]
for block, pose in initial_poses.iteritems():
initial_atoms += [
IsBlock(block),
IsPose(pose),
AtPose(block, pose),
]
goal_literals = []
for block, pose in goal_poses.iteritems():
initial_atoms += [
IsBlock(block),
IsPose(pose),
]
goal_literals.append(AtPose(block, pose))
problem = STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms, cond_streams, constants)
return problem