def main():
parser = argparse.ArgumentParser()
parser.add_argument('-a',
'--algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-o',
'--optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-s',
'--skeleton',
action='store_true',
help='Enforces skeleton plan constraints')
# TODO: test if placed in the same region
defer_fn = defer_shared # never_defer | defer_unique | defer_shared
tamp_problem, args = initialize(parser)
stream_info = {
's-region':
StreamInfo(defer_fn=defer_fn),
's-grasp':
StreamInfo(defer_fn=defer_fn),
's-ik':
StreamInfo(defer_fn=get_defer_all_unbound(
inputs='?g')), # defer_fn | defer_unbound
's-motion':
StreamInfo(defer_fn=get_defer_any_unbound()),
't-cfree':
StreamInfo(defer_fn=get_defer_any_unbound(), eager=False,
negate=True), # defer_fn | defer_unbound
't-region':
StreamInfo(eager=False, p_success=0), # bound_fn is None
'dist':
FunctionInfo(defer_fn=get_defer_any_unbound(),
opt_fn=lambda q1, q2: MOVE_COST),
'gurobi-cfree':
StreamInfo(eager=False, negate=True),
#'gurobi': OptimizerInfo(p_success=0),
#'rrt': OptimizerInfo(p_success=0),
}
hierarchy = [
#ABSTRIPSLayer(pos_pre=['atconf']), #, horizon=1),
]
skeletons = [TIGHT_SKELETON] if args.skeleton else None
assert implies(args.skeleton, args.problem == 'tight')
max_cost = INF # 8*MOVE_COST
constraints = PlanConstraints(
skeletons=skeletons,
#skeletons=[],
#skeletons=[skeleton, []],
exact=True,
max_cost=max_cost)
#replan_actions = set()
replan_actions = {'move', 'pick', 'place'}
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
collisions=not args.cfree,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
dump_pddlstream(pddlstream_problem)
pr = cProfile.Profile()
pr.enable()
success_cost = 0 if args.optimal else INF
planner = 'max-astar'
#planner = 'ff-wastar1'
if args.algorithm == 'focused':
solver = solve_focused # solve_focused | solve_serialized
solution = solver(
pddlstream_problem,
constraints=constraints,
stream_info=stream_info,
replan_actions=replan_actions,
planner=planner,
max_planner_time=10,
hierarchy=hierarchy,
debug=False,
max_time=args.max_time,
max_iterations=INF,
verbose=True,
unit_costs=args.unit,
success_cost=success_cost,
unit_efforts=True,
effort_weight=1,
search_sample_ratio=1,
#max_skeletons=None, bind=True,
visualize=args.visualize)
elif args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
constraints=constraints,
complexity_step=2,
planner=planner,
hierarchy=hierarchy,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=False)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('cumtime').print_stats(20)
if plan is not None:
display_plan(tamp_problem, retime_plan(plan))
def main(success_cost=INF, use_costs=True): # 0 | INF
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-a',
'--algorithm',
default='',
help='Specifies the algorithm')
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-t',
'--max_time',
default=30,
type=int,
help='The max time')
parser.add_argument('-u',
'--unit',
action='store_true',
help='Uses unit costs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
set_deterministic()
print('Random seed:', get_random_seed())
tamp_problem = tight(n_robots=1, n_blocks=2, n_goals=2)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
_, _, stream_pddl, stream_map, _, _ = pddlstream_problem
stream_info = {
't-region': StreamInfo(eager=True, p_success=0), # bound_fn is None
#'t-cfree': StreamInfo(eager=False, negate=True),
#'distance': FunctionInfo(opt_fn=lambda q1, q2: MOVE_COST), # Doesn't make a difference
}
terms = CONSTRAINTS
print('Constraints:', CONSTRAINTS)
if use_costs:
print('Objectives:', OBJECTIVES)
terms += OBJECTIVES
with Profiler():
if args.algorithm == 'focused':
solution = solve_pddlstream_satisfaction(
stream_pddl,
stream_map,
INIT,
terms,
incremental=False,
stream_info=stream_info,
#search_sample_ratio=1,
#max_skeletons=1,
success_cost=success_cost,
max_time=args.max_time)
elif args.algorithm == 'incremental':
assert not args.gurobi
solution = solve_pddlstream_satisfaction(stream_pddl,
stream_map,
INIT,
terms,
incremental=True,
success_cost=success_cost,
max_time=args.max_time,
verbose=False,
debug=False)
else:
solution = constraint_satisfaction(stream_pddl,
stream_map,
INIT,
terms,
stream_info=stream_info,
costs=not args.unit,
success_cost=success_cost,
max_time=args.max_time,
search_sample_ratio=1,
debug=False)
#raise ValueError(args.algorithm)
dump_assignment(solution)
bindings, cost, evaluations = solution
if bindings is None:
return
plan = []
for name, args in SKELETON:
new_args = [bindings[a] if is_parameter(a) else a for a in args]
plan.append((name, new_args))
display_plan(tamp_problem, retime_plan(plan))
def main():
# TODO: side grasps (horizontal gripper, one finger, forklift)
parser = create_parser()
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-o',
'--optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-s',
'--skeleton',
action='store_true',
help='Enforces skeleton plan constraints')
tamp_problem, args = initialize(parser)
# TODO: test if placed in the same region
defer_fn = defer_shared # never_defer | defer_unique | defer_shared
stream_info = {
's-region':
StreamInfo(defer_fn=defer_fn),
's-grasp':
StreamInfo(defer_fn=defer_fn),
's-ik':
StreamInfo(defer_fn=get_defer_all_unbound(
inputs='?g')), # defer_fn | defer_unbound
's-motion':
StreamInfo(defer_fn=get_defer_any_unbound()),
't-cfree':
StreamInfo(defer_fn=get_defer_any_unbound(),
eager=False,
verbose=False), # defer_fn | defer_unbound
't-region':
StreamInfo(eager=True, p_success=0), # bound_fn is None
'dist':
FunctionInfo(eager=False,
defer_fn=get_defer_any_unbound(),
opt_fn=lambda q1, q2: MOVE_COST),
'gurobi-cfree':
StreamInfo(
eager=False, negate=True
), # TODO: AttributeError: 'tuple' object has no attribute 'instance'
#'gurobi': OptimizerInfo(p_success=0),
#'rrt': OptimizerInfo(p_success=0),
}
#stream_info = {}
hierarchy = [
#ABSTRIPSLayer(pos_pre=['atconf']), #, horizon=1),
]
skeletons = [TIGHT_SKELETON] if args.skeleton else None
assert implies(args.skeleton, args.problem == 'tight')
max_cost = INF # 8*MOVE_COST
constraints = PlanConstraints(
skeletons=skeletons,
#skeletons=[],
#skeletons=[skeleton, []],
exact=True,
max_cost=max_cost)
replan_actions = set()
#replan_actions = {'move', 'pick', 'place'}
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
collisions=not args.cfree,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
dump_pddlstream(pddlstream_problem)
success_cost = 0 if args.optimal else INF
#planner = 'dijkstra'
planner = 'max-astar'
#planner = 'ff-wastar1'
#effort_weight = 1.
effort_weight = 1. / get_cost_scale()
#effort_weight = None
with Profiler(field='cumtime', num=20):
solution = solve(pddlstream_problem,
algorithm=args.algorithm,
constraints=constraints,
stream_info=stream_info,
replan_actions=replan_actions,
planner=planner,
max_planner_time=10,
hierarchy=hierarchy,
max_time=args.max_time,
max_iterations=INF,
debug=False,
verbose=True,
unit_costs=args.unit,
success_cost=success_cost,
unit_efforts=True,
effort_weight=effort_weight,
search_sample_ratio=1,
visualize=args.visualize) # TODO: solve_serialized
print_solution(solution)
plan, cost, evaluations = solution
if plan is not None:
display_plan(tamp_problem, retime_plan(plan))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--problem',
default='blocked',
help='The name of the problem to solve')
parser.add_argument('-a',
'--algorithm',
default='focused',
help='Specifies the algorithm')
parser.add_argument('-c',
'--cfree',
action='store_true',
help='Disables collisions')
parser.add_argument('-d',
'--deterministic',
action='store_true',
help='Uses a deterministic sampler')
parser.add_argument('-g',
'--gurobi',
action='store_true',
help='Uses gurobi')
parser.add_argument('-n',
'--number',
default=2,
type=int,
help='The number of blocks')
parser.add_argument('-o',
'--optimal',
action='store_true',
help='Runs in an anytime mode')
parser.add_argument('-s',
'--skeleton',
action='store_true',
help='Enforces skeleton plan constraints')
parser.add_argument('-t',
'--max_time',
default=30,
type=int,
help='The max time')
parser.add_argument('-u',
'--unit',
action='store_true',
help='Uses unit costs')
parser.add_argument('-v',
'--visualize',
action='store_true',
help='Visualizes graphs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
random.seed(seed=0)
np.random.seed(seed=0)
print('Random seed:', get_random_seed())
problem_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_from_name:
raise ValueError(args.problem)
print('Problem:', args.problem)
problem_fn = problem_from_name[args.problem]
tamp_problem = problem_fn(args.number)
print(tamp_problem)
action_info = {
#'move': ActionInfo(terminal=True),
#'pick': ActionInfo(terminal=True),
#'place': ActionInfo(terminal=True),
}
stream_info = {
't-region': StreamInfo(eager=False, p_success=0), # bound_fn is None
't-cfree': StreamInfo(eager=False, negate=True),
'distance': FunctionInfo(opt_fn=lambda q1, q2: MOVE_COST),
'gurobi-cfree': StreamInfo(eager=False, negate=True),
#'gurobi': OptimizerInfo(p_success=0),
#'rrt': OptimizerInfo(p_success=0),
}
hierarchy = [
#ABSTRIPSLayer(pos_pre=['atconf']), #, horizon=1),
]
skeletons = [TIGHT_SKELETON] if args.skeleton else None
max_cost = INF # 8*MOVE_COST
constraints = PlanConstraints(
skeletons=skeletons,
#skeletons=[],
#skeletons=[skeleton, []],
exact=True,
max_cost=max_cost)
pddlstream_problem = pddlstream_from_tamp(tamp_problem,
collisions=not args.cfree,
use_stream=not args.gurobi,
use_optimizer=args.gurobi)
print('Initial:', sorted_str_from_list(pddlstream_problem.init))
print('Goal:', str_from_object(pddlstream_problem.goal))
pr = cProfile.Profile()
pr.enable()
success_cost = 0 if args.optimal else INF
planner = 'max-astar'
#planner = 'ff-wastar1'
if args.algorithm == 'focused':
solution = solve_focused(
pddlstream_problem,
constraints=constraints,
action_info=action_info,
stream_info=stream_info,
planner=planner,
max_planner_time=10,
hierarchy=hierarchy,
debug=False,
max_time=args.max_time,
max_iterations=INF,
verbose=True,
unit_costs=args.unit,
success_cost=success_cost,
unit_efforts=False,
effort_weight=0,
search_sample_ratio=1,
#max_skeletons=None, bind=True,
visualize=args.visualize)
elif args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
constraints=constraints,
complexity_step=2,
planner=planner,
hierarchy=hierarchy,
unit_costs=args.unit,
success_cost=success_cost,
max_time=args.max_time,
verbose=False)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('cumtime').print_stats(20)
if plan is not None:
display_plan(tamp_problem, retime_plan(plan))