def rmpyl_episode_ids(hello,uav):
"""Example of how episode ID's can be used to retrieve them."""
prog = RMPyL()
first_uav_seq = prog.sequence(uav.scan(),uav.fly(),id='uav-1-seq')
second_uav_seq = prog.sequence(uav.scan(),uav.fly(),id='uav-2-seq')
first_hello_seq = prog.sequence(hello.scan(),hello.fly(),id='hello-1-seq')
second_hello_seq = prog.sequence(hello.scan(),hello.fly(),id='hello-2-seq')
prog *= prog.parallel(prog.sequence(first_uav_seq,second_uav_seq,id='uav-seqs'),
prog.sequence(first_hello_seq,second_hello_seq,id='hello-seqs'),id='par-seqs')
#This could have been accomplished much more easily by using the sequence
#variables directly, but I wanted to show how episodes can be retrieved by
#ID.
tc1 = TemporalConstraint(start=prog.episode_by_id('uav-1-seq').end,
end=prog.episode_by_id('hello-2-seq').start,
ctype='controllable',lb=2.0,ub=3.0)
tc2 = TemporalConstraint(start=prog.episode_by_id('hello-1-seq').end,
end=prog.episode_by_id('uav-2-seq').start,
ctype='controllable',lb=0.5,ub=1.0)
prog.add_temporal_constraint(tc1)
prog.add_temporal_constraint(tc2)
return prog
def to_rmpyl(tcs):
"""
Converts the temporal constraints into an RMPyL program, which can then
be exported to a TPN.
"""
prog = RMPyL()
for tc in tcs:
prog.add_temporal_constraint(tc)
return prog
def rmpyl_parallel_user_defined_tcs(hello,uav):
"""Simple RMPyL example with parallel execution of actions with user-defined
constraints."""
prog = RMPyL()
hello_flight = hello.fly()
uav_flight = uav.fly()
uav_scan = uav.scan()
prog *= hello_flight+(uav_scan*uav_flight)
tc1 = TemporalConstraint(start=uav_scan.end,end=hello_flight.start,
ctype='controllable',lb=2.0,ub=3.0)
tc2 = TemporalConstraint(start=hello_flight.end,end=uav_flight.start,
ctype='controllable',lb=3.0,ub=4.0)
for tc in [tc1,tc2]:
prog.add_temporal_constraint(tc)
prog.add_overall_temporal_constraint(ctype='controllable',lb=0.0,ub=50.0)
return prog
def rmpyl_inconsistent_user_defined_tcs(hello,uav):
"""Simple RMPyL example with parallel execution of actions on different choice
branches with user-defined constraints."""
try:
prog = RMPyL()
hello_flights = [hello.fly() for i in range(2)] #two different hello flights
uav_flights = [uav.fly() for i in range(2)] #two different uav flights
#Choice using the previous example as a subroutine to generate partial plans
prog *= prog.decide({'name':'Choose-branch','domain':['1','2'],'utility':[1,2]},
hello_flights[0]*uav_flights[0],
uav_flights[1]*hello_flights[1])
tc = TemporalConstraint(start=hello_flights[0].end,end=hello_flights[1].start,
ctype='controllable',lb=2.0,ub=3.0)
#This is a constraint between disjoint plan branches. Therefore, it MUST
#cause an error of empty constraint support
prog.add_temporal_constraint(tc)
except InconsistentSupportError:
print('Empty support error correctly caught!')
prog=None
return prog
elapsed = time.time()-start
print('\n##### All solving took %.4f s'%(elapsed))
prog = RMPyL(name='run()')
pddl_episodes = [Episode(id=make_episode_id(t,op),
start=Event(name='start-of-%d-%s'%(t,op)),
end=Event(name='end-of-%d-%s'%(t,op)),
action=op,
duration=rss_duration_model_func(op)) for t,op in enumerate(plan)]
prog.plan = prog.sequence(*pddl_episodes)
# prog.add_overall_temporal_constraint(ctype='controllable',lb=0.0,ub=2000.0)
#Adds temporal window to the plan
for t,op in enumerate(plan):
bounds, tc_type = rss_time_window_model_func(op)
for tc in time_window_constraints(tc_type,bounds,prog.first_event,prog.episode_by_id(make_episode_id(t,op))):
prog.add_temporal_constraint(tc)
#Dummy episodes that enable transmissions
activation_episodes=[]
activation_tcs=[]
global_start=Event(name='global-start')
for op_name,op_param_dict in time_windows['time_windows'].items():
for arg_set,window_dict in op_param_dict.items():
for ev_type,time_bound in window_dict.items():
orb_ep_id='%s_event_%s-%s'%(ev_type,op_name,'-'.join(arg_set))
activation_episodes.append(Episode(id=orb_ep_id,
action='(%s)'%(orb_ep_id.replace('-',' ')),
duration={'ctype':'controllable','lb':0.005,'ub':0.1}))
activation_tcs.append(TemporalConstraint(start=global_start,
end=activation_episodes[-1].start,
def rmpyl_breakfast():
"""
Example from (Levine & Williams, ICAPS14).
"""
#Actions that Alice performs
get_mug_ep = Episode(action='(get alice mug)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_glass_ep = Episode(action='(get alice glass)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
make_cofee_ep = Episode(action='(make-coffee alice)',
duration={
'ctype': 'controllable',
'lb': 3.0,
'ub': 5.0
})
pour_cofee_ep = Episode(action='(pour-coffee alice mug)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
pour_juice_glass = Episode(action='(pour-juice alice glass)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_bagel_ep = Episode(action='(get alice bagel)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_cereal_ep = Episode(action='(get alice cereal)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
toast_bagel_ep = Episode(action='(toast alice bagel)',
duration={
'ctype': 'controllable',
'lb': 3.0,
'ub': 5.0
})
add_cheese_bagel_ep = Episode(action='(add-cheese alice bagel)',
duration={
'ctype': 'controllable',
'lb': 1.0,
'ub': 2.0
})
mix_cereal_ep = Episode(action='(mix-cereal alice milk)',
duration={
'ctype': 'controllable',
'lb': 1.0,
'ub': 2.0
})
#Actions that the robot performs
get_grounds_ep = Episode(action='(get grounds robot)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_juice_ep = Episode(action='(get juice robot)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_milk_ep = Episode(action='(get milk robot)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
get_cheese_ep = Episode(action='(get cheese robot)',
duration={
'ctype': 'controllable',
'lb': 0.5,
'ub': 1.0
})
prog = RMPyL()
prog *= prog.sequence(
prog.parallel(
prog.observe(
{
'name': 'observe-utensil',
'domain': ['Mug', 'Glass'],
'ctype': 'uncontrollable'
},
get_mug_ep,
get_glass_ep,
id='observe-utensil-ep'),
prog.decide(
{
'name': 'choose-beverage-ingredient',
'domain': ['Grounds', 'Juice'],
'utility': [0, 0]
},
get_grounds_ep,
get_juice_ep,
id='choose-beverage-ingredient-ep')),
prog.observe(
{
'name': 'observe-alice-drink',
'domain': ['Coffee', 'Juice'],
'ctype': 'uncontrollable'
},
prog.sequence(make_cofee_ep, pour_cofee_ep),
pour_juice_glass,
id='observe-alice-drink-ep'),
prog.parallel(prog.observe(
{
'name': 'observe-food',
'domain': ['Bagel', 'Cereal'],
'ctype': 'uncontrollable'
},
get_bagel_ep,
get_cereal_ep,
id='observe-food-ep'),
prog.decide(
{
'name': 'choose-food-ingredient',
'domain': ['Milk', 'Cheese'],
'utility': [0, 0]
},
get_milk_ep,
get_cheese_ep,
id='choose-food-ingredient-ep'),
id='parallel-food-ep'),
prog.observe(
{
'name': 'observe-alice-food',
'domain': ['Bagel', 'Cereal'],
'ctype': 'uncontrollable'
}, prog.sequence(toast_bagel_ep, add_cheese_bagel_ep),
mix_cereal_ep),
id='breakfast-sequence')
extra_tcs = [
TemporalConstraint(
start=prog.episode_by_id('breakfast-sequence').start,
end=prog.episode_by_id('observe-utensil-ep').start,
ctype='controllable',
lb=0.0,
ub=0.0),
TemporalConstraint(
start=prog.episode_by_id('breakfast-sequence').start,
end=prog.episode_by_id('choose-beverage-ingredient-ep').start,
ctype='controllable',
lb=0.2,
ub=0.3),
TemporalConstraint(start=prog.episode_by_id('parallel-food-ep').start,
end=prog.episode_by_id('observe-food-ep').start,
ctype='controllable',
lb=0.0,
ub=0.0),
TemporalConstraint(
start=prog.episode_by_id('parallel-food-ep').start,
end=prog.episode_by_id('choose-food-ingredient-ep').start,
ctype='controllable',
lb=0.2,
ub=0.3)
]
for tc in extra_tcs:
prog.add_temporal_constraint(tc)
prog.add_overall_temporal_constraint(ctype='controllable', lb=0.0, ub=7.0)
prog.simplify_temporal_constraints()
return prog
def rmpyl_breakfast():
"""
Example from (Levine & Williams, ICAPS14).
"""
#Actions that Alice performs
get_mug_ep = Episode(action='(get alice mug)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_glass_ep = Episode(action='(get alice glass)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
make_cofee_ep = Episode(action='(make-coffee alice)',duration={'ctype':'controllable','lb':3.0,'ub':5.0})
pour_cofee_ep = Episode(action='(pour-coffee alice mug)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
pour_juice_glass = Episode(action='(pour-juice alice glass)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_bagel_ep = Episode(action='(get alice bagel)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_cereal_ep = Episode(action='(get alice cereal)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
toast_bagel_ep = Episode(action='(toast alice bagel)',duration={'ctype':'controllable','lb':3.0,'ub':5.0})
add_cheese_bagel_ep = Episode(action='(add-cheese alice bagel)',duration={'ctype':'controllable','lb':1.0,'ub':2.0})
mix_cereal_ep = Episode(action='(mix-cereal alice milk)',duration={'ctype':'controllable','lb':1.0,'ub':2.0})
#Actions that the robot performs
get_grounds_ep = Episode(action='(get grounds robot)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_juice_ep = Episode(action='(get juice robot)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_milk_ep = Episode(action='(get milk robot)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
get_cheese_ep = Episode(action='(get cheese robot)',duration={'ctype':'controllable','lb':0.5,'ub':1.0})
prog = RMPyL()
prog *= prog.sequence(
prog.parallel(
prog.observe(
{'name':'observe-utensil','domain':['Mug','Glass'],'ctype':'uncontrollable'},
get_mug_ep,
get_glass_ep,
id='observe-utensil-ep'),
prog.decide(
{'name':'choose-beverage-ingredient','domain':['Grounds','Juice'],'utility':[0,0]},
get_grounds_ep,
get_juice_ep,
id='choose-beverage-ingredient-ep')),
prog.observe(
{'name':'observe-alice-drink','domain':['Coffee','Juice'],'ctype':'uncontrollable'},
prog.sequence(make_cofee_ep,pour_cofee_ep),
pour_juice_glass,
id='observe-alice-drink-ep'),
prog.parallel(
prog.observe(
{'name':'observe-food','domain':['Bagel','Cereal'],'ctype':'uncontrollable'},
get_bagel_ep,
get_cereal_ep,
id='observe-food-ep'),
prog.decide(
{'name':'choose-food-ingredient','domain':['Milk','Cheese'],'utility':[0,0]},
get_milk_ep,
get_cheese_ep,
id='choose-food-ingredient-ep'),
id='parallel-food-ep'),
prog.observe(
{'name':'observe-alice-food','domain':['Bagel','Cereal'],'ctype':'uncontrollable'},
prog.sequence(toast_bagel_ep,add_cheese_bagel_ep),
mix_cereal_ep),
id='breakfast-sequence')
extra_tcs = [TemporalConstraint(start=prog.episode_by_id('breakfast-sequence').start,
end=prog.episode_by_id('observe-utensil-ep').start,
ctype='controllable',lb=0.0,ub=0.0),
TemporalConstraint(start=prog.episode_by_id('breakfast-sequence').start,
end=prog.episode_by_id('choose-beverage-ingredient-ep').start,
ctype='controllable',lb=0.2,ub=0.3),
TemporalConstraint(start=prog.episode_by_id('parallel-food-ep').start,
end=prog.episode_by_id('observe-food-ep').start,
ctype='controllable',lb=0.0,ub=0.0),
TemporalConstraint(start=prog.episode_by_id('parallel-food-ep').start,
end=prog.episode_by_id('choose-food-ingredient-ep').start,
ctype='controllable',lb=0.2,ub=0.3)]
for tc in extra_tcs:
prog.add_temporal_constraint(tc)
prog.add_overall_temporal_constraint(ctype='controllable',lb=0.0,ub=7.0)
prog.simplify_temporal_constraints()
return prog
Episode(id=make_episode_id(t, op),
start=Event(name='start-of-%d-%s' % (t, op)),
end=Event(name='end-of-%d-%s' % (t, op)),
action=op,
duration=rss_duration_model_func(op))
for t, op in enumerate(plan)
]
prog.plan = prog.sequence(*pddl_episodes)
# prog.add_overall_temporal_constraint(ctype='controllable',lb=0.0,ub=2000.0)
#Adds temporal window to the plan
for t, op in enumerate(plan):
bounds, tc_type = rss_time_window_model_func(op)
for tc in time_window_constraints(
tc_type, bounds, prog.first_event,
prog.episode_by_id(make_episode_id(t, op))):
prog.add_temporal_constraint(tc)
#Dummy episodes that enable transmissions
activation_episodes = []
activation_tcs = []
global_start = Event(name='global-start')
for op_name, op_param_dict in time_windows['time_windows'].items():
for arg_set, window_dict in op_param_dict.items():
for ev_type, time_bound in window_dict.items():
orb_ep_id = '%s_event_%s-%s' % (ev_type, op_name,
'-'.join(arg_set))
activation_episodes.append(
Episode(id=orb_ep_id,
action='(%s)' % (orb_ep_id.replace('-', ' ')),
duration={