def main(focused=True, unit_costs=False):
problem_fn = get_shift_one_problem # get_shift_one_problem | get_shift_all_problem
tamp_problem = problem_fn()
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem)
if focused:
solution = solve_focused(pddlstream_problem, unit_costs=unit_costs)
else:
#solution = solve_exhaustive(pddlstream_problem, unit_costs=unit_costs)
solution = solve_incremental(pddlstream_problem, unit_costs=unit_costs)
print_solution(solution)
plan, cost, evaluations = solution
if plan is None:
return
print(evaluations)
colors = dict(zip(tamp_problem.initial.block_poses, COLORS))
viewer = DiscreteTAMPViewer(1, len(tamp_problem.poses), title='Initial')
state = tamp_problem.initial
print(state)
draw_state(viewer, state, colors)
for action in plan:
user_input('Continue?')
state = apply_action(state, action)
print(state)
draw_state(viewer, state, colors)
user_input('Finish?')
def solve_pddlstream(focused=True):
pddlstream_problem = get_problem()
if focused:
solution = solve_focused(pddlstream_problem, unit_costs=True)
else:
#solution = solve_exhaustive(pddlstream_problem, unit_costs=True)
solution = solve_incremental(pddlstream_problem, unit_costs=True)
print_solution(solution)
def solve_pddlstream(focused=True):
problem_fn = get_problem1 # get_problem1 | get_problem2
pddlstream_problem = problem_fn()
print('Init:', pddlstream_problem.init)
print('Goal:', pddlstream_problem.goal)
if focused:
solution = solve_focused(pddlstream_problem, unit_costs=True)
else:
solution = solve_incremental(pddlstream_problem, unit_costs=True)
print_solution(solution)
def main(focused=True):
# TODO: maybe load problems as a domain explicitly
pddlstream_problem = pddlstream_from_belief()
_, _, _, _, init, goal = pddlstream_problem
print(sorted(init, key=lambda f: f[0]))
print(goal)
pr = cProfile.Profile()
pr.enable()
if focused:
solution = solve_focused(pddlstream_problem, unit_costs=False)
else:
#solution = solve_exhaustive(pddlstream_problem, unit_costs=False)
solution = solve_incremental(pddlstream_problem, unit_costs=False)
print_solution(solution)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
def main(max_time=20):
"""
Creates and solves the 2D motion planning problem.
"""
obstacles = [create_box((.5, .5), (.2, .2))]
regions = {
'env': create_box((.5, .5), (1, 1)),
#'goal': create_box((.8, .8), (.4, .4)),
}
goal = np.array([.65, .65])
# goal = 'goal'
max_distance = 0.25 # 0.2 | 0.25 | 0.5 | 1.0
problem, roadmap = create_problem(goal,
obstacles,
regions,
max_distance=max_distance)
pr = cProfile.Profile()
pr.enable()
solution = solve_incremental(problem,
unit_costs=False,
max_cost=0,
max_time=max_time,
verbose=False)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
print_solution(solution)
plan, cost, evaluations = solution
print('Plan:', plan)
if plan is None:
return
# TODO: use the same viewer here
draw_roadmap(roadmap, obstacles, regions) # TODO: do this in realtime
user_input('Continue?')
segments = [args for name, args in plan]
draw_solution(segments, obstacles, regions)
user_input('Finish?')
def main(deterministic=False,
observable=False,
collisions=True,
focused=True,
factor=True):
# TODO: global search over the state
belief_problem = get_belief_problem(deterministic, observable)
pddlstream_problem = to_pddlstream(belief_problem, collisions)
pr = cProfile.Profile()
pr.enable()
planner = 'ff-wastar1'
if focused:
stream_info = {
'GE': StreamInfo(from_test(ge_fn), eager=False),
'prob-after-move':
StreamInfo(from_fn(get_opt_move_fn(factor=factor))),
'MoveCost': FunctionInfo(move_cost_fn),
'prob-after-look':
StreamInfo(from_fn(get_opt_obs_fn(factor=factor))),
'LookCost': FunctionInfo(get_look_cost_fn(p_look_fp=0,
p_look_fn=0)),
}
solution = solve_focused(pddlstream_problem,
stream_info=stream_info,
planner=planner,
debug=False,
max_cost=0,
unit_costs=False,
max_time=30)
else:
solution = solve_incremental(pddlstream_problem,
planner=planner,
debug=True,
max_cost=MAX_COST,
unit_costs=False,
max_time=30)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
print_solution(solution)
plan, cost, init = solution
print('Real cost:', float(cost) / SCALE_COST)
def main():
uniform_rooms = UniformDist(['room0', OTHER])
#uniform_tables = UniformDist(['table0', 'table1'])
#uniform_tables = UniformDist(['table0', OTHER])
uniform_tables = UniformDist(['table0', 'table1', OTHER])
#initial_belief = get_room_belief(uniform_rooms, uniform_tables, 1.0)
initial_belief = get_room_belief(uniform_rooms, uniform_tables, 0.2)
#initial_belief = get_table_belief(uniform_tables, 1.0)
#initial_belief = get_table_belief(uniform_tables, 0.2)
#initial_belief = get_item_belief()
pddlstream_problem = pddlstream_from_belief(initial_belief)
_, _, _, _, init, goal = pddlstream_problem
print(sorted(init))
print(goal)
pr = cProfile.Profile()
pr.enable()
solution = solve_incremental(pddlstream_problem, unit_costs=False)
print_solution(solution)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
def main(focused=True, deterministic=False, unit_costs=False):
np.set_printoptions(precision=2)
if deterministic:
seed = 0
np.random.seed(seed)
print('Seed:', get_random_seed())
problem_fn = get_blocked_problem # get_tight_problem | get_blocked_problem
tamp_problem = problem_fn()
print(tamp_problem)
action_info = {
#'move': ActionInfo(terminal=True),
#'pick': ActionInfo(terminal=True),
#'place': ActionInfo(terminal=True),
}
stream_info = {
#'test-region': StreamInfo(eager=True, p_success=0), # bound_fn is None
#'plan-motion': StreamInfo(p_success=1), # bound_fn is None
#'trajcollision': StreamInfo(p_success=1), # bound_fn is None
#'cfree': StreamInfo(eager=True),
}
dynamic = [
StreamSynthesizer('cfree-motion', {
'plan-motion': 1,
'trajcollision': 0
},
gen_fn=from_fn(cfree_motion_fn)),
#StreamSynthesizer('optimize', {'sample-pose': 1, 'inverse-kinematics': 1,
# 'posecollision': 0, 'distance': 0},
# gen_fn=from_fn(get_optimize_fn(tamp_problem.regions))),
]
pddlstream_problem = pddlstream_from_tamp(tamp_problem)
pr = cProfile.Profile()
pr.enable()
if focused:
solution = solve_focused(pddlstream_problem,
action_info=action_info,
stream_info=stream_info,
synthesizers=dynamic,
max_time=10,
max_cost=INF,
debug=False,
effort_weight=None,
unit_costs=unit_costs,
postprocess=False,
visualize=False)
else:
solution = solve_incremental(pddlstream_problem,
layers=1,
unit_costs=unit_costs)
print_solution(solution)
plan, cost, evaluations = solution
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
if plan is None:
return
colors = dict(zip(sorted(tamp_problem.initial.block_poses.keys()), COLORS))
viewer = ContinuousTMPViewer(tamp_problem.regions, title='Continuous TAMP')
state = tamp_problem.initial
print()
print(state)
draw_state(viewer, state, colors)
for i, action in enumerate(plan):
user_input('Continue?')
print(i, *action)
state = apply_action(state, action)
print(state)
draw_state(viewer, state, colors)
user_input('Finish?')