def change_start(self, map, act):
if map != self.task.additions[0][max(self.task.additions[0])]:
place = self.task.additions[0][max(
self.task.additions[0])]['objects'][self.name]
self.task.additions[0][max(self.task.additions[0])] = map
self.task.additions[0][max(
self.task.additions[0])]['objects'][self.name] = place
region_map, cell_map_pddl, cell_location, near_loc, cell_coords, size, cl_lv = signs_markup(
self.task.additions[0][max(self.task.additions[0])],
self.task.additions[3], self.name)
agent_state_action = state_prediction(
self.task.signs[self.name],
self.task.additions[0][max(self.task.additions[0])],
self.task.signs)
active_situation = define_situation(
'*start-sit-*-' + act + '-' + self.name, cell_map_pddl,
self.task.additions[0][max(
self.task.additions[0])]['conditions'], agent_state_action,
self.task.signs)
active_map = define_map('*start-map-*-' + act + '-' + self.name,
region_map, cell_location, near_loc,
self.task.additions[1], self.task.signs)
state_fixation(active_situation, cell_coords, self.task.signs,
'cell')
I_img = self.task.signs['I'].add_image()
active_situation.replace('image', self.task.signs[self.name],
I_img)
self.task.start_situation = active_situation.sign
self.task.start_map = active_map.sign
def create_situation(description, agent, whose='They', size_prev=None):
region_map, cell_map_pddl, cell_location, near_loc, cell_coords, size, cl_lv = ut.signs_markup(
description, self.additions[3], agent.name, size=size_prev)
if cl_lv < description['cl_lv'] and not size_prev:
#it can be only iff actions's cl_lv more than after markup
agcx = description['objects'][agent.name]['x']
agcy = description['objects'][agent.name]['y']
while cl_lv != description['cl_lv']:
size_x = (size[2] - size[0]) // 6
size_y = (size[3] - size[1]) // 6
size = [
agcx - size_x, agcy - size_y, agcx + size_x,
agcy + size_y
]
cl_lv += 1
region_map, cell_map_pddl, cell_location, near_loc, cell_coords, size, cl_lv = ut.signs_markup(
description, self.additions[3], agent.name, size=size)
agent_state_action = ut.state_prediction(agent, description,
self.signs)
conditions_new = description['conditions']
global SIT_COUNTER
SIT_COUNTER += 1
act_sit = ut.define_situation(
'situation_' + whose + '_' + str(SIT_COUNTER), cell_map_pddl,
conditions_new, agent_state_action, self.signs)
act_map = ut.define_map('map_' + whose + '_' + str(SIT_COUNTER),
region_map, cell_location, near_loc,
self.additions[1], self.signs)
ut.state_fixation(act_sit, cell_coords, self.signs, 'cell')
return act_sit, act_map, size
def get_spatial_finish_blocks(self, cur_sit, cl_lv, size, ag_name):
sit = deepcopy(self.task.goal_state)
ag_pose = sit['objects'][self.name]
sit['objects'] = deepcopy(cur_sit['objects'])
sit['objects'][self.name] = ag_pose
region_map, cell_map, cell_location, near_loc, cell_coords, size, cl_lv = signs_markup(
sit, self.task.additions[3], ag_name, size=size, cl_lv=cl_lv)
agent_state_action = state_prediction(self.task.signs[ag_name], sit,
self.task.signs)
conditions = sit['conditions']
sit['conditions'] = conditions
action_situation = define_situation('*goal-sit*-' + ag_name, cell_map,
conditions, agent_state_action,
self.task.signs)
action_map = define_map('*goal-map-*-' + ag_name, region_map,
cell_location, near_loc,
self.task.additions[1], self.task.signs)
state_fixation(action_situation, cell_coords, self.task.signs, 'cell')
return action_situation, action_map, cl_lv, sit
def copy_action(self, act, agent, old_sit):
from mapspatial.grounding.utils import pm_parser
new_act = None
if act[1] == 'Clarify' or act[1] == 'Abstract':
change_name = act[1]
cell_coords = old_sit.sign.images[2].spread_down_activity_view(1)
objects = act[-1]['objects']
map_size = self.problem.map['map-size']
borders = self.problem.map['wall']
if change_name == 'Clarify':
var = cell_coords['cell-4']
x_size = (var[2] - var[0]) / 6
y_size = (var[3] - var[1]) / 6
size = [
objects[agent.name]['x'] - x_size,
objects[agent.name]['y'] - y_size,
objects[agent.name]['x'] + x_size,
objects[agent.name]['y'] + y_size
]
else:
size = [
cell_coords['cell-0'][0], cell_coords['cell-0'][1],
cell_coords['cell-8'][2], cell_coords['cell-8'][3]
]
rmap = [0, 0]
rmap.extend(map_size)
# division into regions and cells
region_location, region_map = locater('region-', rmap, objects,
borders)
cell_location, cell_map, near_loc, cell_coords, clar_lv = cell_creater(
size, objects, region_location, borders)
old_sit_new_mean = old_sit.copy('image', 'meaning')
global SIT_SUF
SIT_SUF += 1
conditions, direction, holding = pm_parser(old_sit.sign.images[1],
agent.name,
self.task.signs,
base='image')
agent_state = state_prediction(agent, direction, holding)
active_sit_new = define_situation('situation_they_' + str(SIT_SUF),
cell_map, conditions,
agent_state, self.task.signs)
active_sit_new_mean = active_sit_new.copy('image', 'meaning')
new_act = self.task.signs[change_name].add_meaning()
connector = new_act.add_feature(old_sit_new_mean)
old_sit.sign.add_out_meaning(connector)
connector = new_act.add_feature(active_sit_new_mean, effect=True)
active_sit_new.sign.add_out_meaning(connector)
else:
act_mean = act[2].spread_down_activity('meaning', 3)
act_signs = set()
for pm_list in act_mean:
act_signs |= set(
[c.sign.name for c in pm_list if c.sign.name != 'I'])
act_signs.add(agent.name)
action_pms = self.task.signs[act[1]].meanings
for index, cm in action_pms.items():
cm_mean = cm.spread_down_activity('meaning', 3)
cm_signs = set()
for cm_list in cm_mean:
cm_signs |= set([c.sign.name for c in cm_list])
if act_signs == cm_signs:
new_act = cm
break
return new_act
def get_spatial_sit_blocks(self, action, cur_sit, prev_size, prev_cl):
if action[3].name == 'I':
ag_name = self.name
else:
ag_name = action[3].name
new_sit = deepcopy(cur_sit)
ag_pos = new_sit['objects'][ag_name]
ag_activity = new_sit[ag_name]['activity']
ground_block = None
clear_block = None
move_block = None
# we need to know coords of block in effect
for el in action[2].effect:
el_signs = el.get_signs()
if (action[1] == 'pick-up' or action[1] == 'unstack')\
and 'holding' in [sign.name for sign in el_signs]:
move_block = [
sign for sign in el_signs
if sign.name != 'holding' and sign.name != ag_name
][0]
break
elif (action[1] == 'stack'
or action[1] == 'put-down') and 'clear' in [
sign.name for sign in el_signs
]:
clear_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'stack':
for el in action[2].cause:
el_signs = el.get_signs()
if 'clear' in [sign.name for sign in el_signs]:
ground_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'unstack':
for el in action[2].effect:
el_signs = el.get_signs()
if 'clear' in [sign.name for sign in el_signs]:
ground_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'put-down' or action[1] == 'stack':
obj = clear_block
else:
obj = move_block
def get_pos(ag_pos, obj_pos):
"""
Thus function was implemented to foresee the agent's position
after it will manipulate with object.
"""
if ag_pos['x'] < obj_pos['x']:
if ag_pos['y'] < obj_pos['y'] - ag_pos['r']:
x = obj_pos['x'] - obj_pos['r'] - 4 * ag_pos['r']
y = obj_pos['y'] - obj_pos['r'] - 4 * ag_pos['r']
orient = 'below-right'
elif ag_pos['y'] > obj_pos['y'] + ag_pos['r']:
x = obj_pos['x'] - obj_pos['r'] - 10 * ag_pos['r']
y = obj_pos['y'] + obj_pos['r'] + 10 * ag_pos['r']
orient = 'above-right'
else:
x = obj_pos['x'] - obj_pos['r'] - 4 * ag_pos['r']
y = obj_pos['y']
orient = 'right'
elif ag_pos['x'] > obj_pos['x']:
if ag_pos['y'] < obj_pos['y'] - ag_pos['r']:
x = obj_pos['x'] + obj_pos['r'] + 4 * ag_pos['r']
y = obj_pos['y'] - obj_pos['r'] - 4 * ag_pos['r']
orient = 'below-left'
elif ag_pos['y'] > obj_pos['y'] + ag_pos['r']:
x = obj_pos['x'] + obj_pos['r'] + 4 * ag_pos['r']
y = obj_pos['y'] + obj_pos['r'] + 4 * ag_pos['r']
orient = 'above-left'
else:
x = obj_pos['x'] + obj_pos['r'] + 2 * ag_pos['r']
y = obj_pos['y']
orient = 'left'
else:
if ag_pos['y'] < obj_pos['y']:
x = ag_pos['x']
y = obj_pos['y'] - obj_pos['r'] - 2 * ag_pos['r']
orient = 'below'
else:
x = ag_pos['x']
y = obj_pos['y'] + obj_pos['r'] + 2 * ag_pos['r']
orient = 'above'
return x, y, orient
# todo change to set of applicable poses
size = None
cl_lv = 0
if action[1] == 'pick-up' or action[1] == 'unstack':
obj_pos = new_sit['objects'][obj.name]
x, y, orient = get_pos(ag_pos, obj_pos)
ag_pos['x'] = x
ag_pos['y'] = y
# check old size, on which agent can perform an action
_, _, _, _, _, size, cl_lv = signs_markup(new_sit,
self.task.additions[3],
ag_name)
new_sit['objects'].pop(obj.name)
for pred, signif in new_sit[ag_name].copy().items():
if pred == 'orientation':
new_sit[ag_name].pop(pred)
new_sit[ag_name][pred] = orient
elif pred == 'handempty':
new_sit[ag_name].pop(pred)
new_sit[ag_name]['holding'] = {
'cause': [ag_name, move_block.name],
'effect': []
}
elif action[1] == 'put-down':
new_sit['objects'][obj.name] = {}
obj_pos = new_sit['objects'][obj.name]
obj_pos['x'] = ag_pos['x'] + 2 * ag_pos['r'] + ag_activity // 2
obj_pos['y'] = ag_pos['y'] + 2 * ag_pos['r'] + ag_activity // 2
obj_pos['r'] = self.task.additions[0][0]['objects'][obj.name]['r']
elif action[1] == 'stack':
ground_coords = new_sit['objects'][ground_block.name]
x, y, orient = get_pos(ag_pos, ground_coords)
# if we need to move, or stay on prev position
if math.sqrt((ag_pos['y'] - y)**2 +
(ag_pos['x'] - x)**2) >= ag_pos['r'] * 2:
ag_pos['x'] = x
ag_pos['y'] = y
new_sit['objects'][obj.name] = {}
obj_pos = new_sit['objects'][obj.name]
obj_pos['x'] = ground_coords['x']
obj_pos['y'] = ground_coords['y']
obj_pos['r'] = self.task.additions[0][0]['objects'][obj.name]['r']
for pred, signif in new_sit[ag_name].copy().items():
if pred == 'orientation':
new_sit[ag_name].pop(pred)
new_sit[ag_name][pred] = orient
elif pred == 'holding':
new_sit[ag_name].pop(pred)
new_sit[ag_name]['handempty'] = {'cause': [], 'effect': []}
region_map, cell_map_pddl, cell_location, near_loc, cell_coords, size, cl_lv = signs_markup(
new_sit, self.task.additions[3], ag_name, size=size, cl_lv=cl_lv)
agent_state_action = state_prediction(self.task.signs[ag_name],
new_sit, self.task.signs)
conditions_new = get_conditions(new_sit, action, obj.name,
ground_block)
new_sit['conditions'] = conditions_new
action_situation = define_situation('*goal-sit-*-' + action[1],
cell_map_pddl, conditions_new,
agent_state_action,
self.task.signs)
action_map = define_map('*goal-map-*-' + action[1], region_map,
cell_location, near_loc,
self.task.additions[1], self.task.signs)
state_fixation(action_situation, cell_coords, self.task.signs, 'cell')
return action_situation, action_map, cl_lv, new_sit, size
def spatial_ground(problem, plagent, agents, exp_signs=None, backward = False):
global signs
initial_state = problem.initial_state
initial_state.update(problem.map)
goal_state = problem.goal_state
goal_state.update(problem.map)
# Prepare states to plagent
init_state = {key: value for key, value in deepcopy(initial_state).items() if key != plagent}
init_state['I'] = initial_state[plagent]
init_state['objects']['I'] = init_state['objects'].pop(plagent)
go_state = {key: value for key, value in deepcopy(goal_state).items() if key != plagent}
go_state['I'] = goal_state[plagent]
go_state['objects']['I'] = go_state['objects'].pop(plagent)
agents.remove(plagent)
agents.append('I')
obj_signifs = {}
obj_means = {}
# Create agents and communications
agent_type = None
types = problem.domain['types']
roles = problem.domain['roles']
if exp_signs:
signs, types = ut._clear_model(exp_signs, signs, types)
signs, roles = ut._clear_model(exp_signs, signs, roles)
I_sign = exp_signs['I']
signs['I'] = I_sign
They_sign = exp_signs['They']
signs['They'] = They_sign
Clarify = exp_signs['Clarify']
signs['Clarify'] = Clarify
Abstract = exp_signs['Abstract']
signs['Abstract'] = Abstract
signs['situation'] = exp_signs['situation']
else:
I_sign = Sign("I")
They_sign = Sign("They")
obj_means[I_sign] = I_sign.add_meaning()
obj_signifs[I_sign] = I_sign.add_significance()
signs[I_sign.name] = I_sign
obj_means[They_sign] = They_sign.add_meaning()
obj_signifs[They_sign] = They_sign.add_significance()
signs[They_sign.name] = They_sign
Clarify = Sign('Clarify')
signs[Clarify.name] = Clarify
Abstract = Sign('Abstract')
signs[Abstract.name] = Abstract
signs['situation'] = Sign('situation')
# ground types
for type_name, smaller in types.items():
if not type_name in signs and not exp_signs:
type_sign = Sign(type_name)
signs[type_name] = type_sign
else:
if exp_signs:
type_sign = exp_signs[type_name]
else:
type_sign = signs[type_name]
if smaller:
for obj in smaller:
if not obj in signs:
obj_sign = Sign(obj)
signs[obj] = obj_sign
else:
obj_sign = signs[obj]
obj_signif = obj_sign.add_significance()
obj_signifs[obj_sign] = obj_signif
tp_signif = type_sign.add_significance()
connector = tp_signif.add_feature(obj_signif, zero_out=True)
obj_sign.add_out_significance(connector)
# Assign I to agent
if obj_sign.name == plagent:
connector = obj_signif.add_feature(obj_signifs[I_sign], zero_out=True)
I_sign.add_out_significance(connector)
agent_type = type_name
else:
obj_signifs[type_sign] = type_sign.add_significance()
# Assign other agents
others = {signs[ag] for ag in agents if ag != 'I'}
for subagent in others:
if not They_sign in subagent.significances[1].get_signs():
signif = obj_signifs[They_sign]
if signif.is_empty():
They_signif = signif
else:
They_signif = They_sign.add_significance()
connector = subagent.significances[1].add_feature(They_signif, zero_out=True)
They_sign.add_out_significance(connector)
obj_means[subagent] = subagent.add_meaning()
# Signify roles
for role_name, smaller in roles.items():
role_sign = Sign(role_name)
signs[role_name] = role_sign
for object in smaller:
obj_sign = signs[object]
obj_signif = obj_sign.significances[1]
role_signif = role_sign.add_significance()
connector = role_signif.add_feature(obj_signif, zero_out=True)
obj_sign.add_out_significance(connector)
if object == agent_type:
agent_type = role_name
# Find and signify walls
if exp_signs:
if not 'wall' in signs:
signs['wall'] = exp_signs['wall']
ws = signs['wall']
views = []
if ws.images:
for num, im in ws.images.items():
if len(im.cause):
for view in im.cause[0].coincidences:
if view.view:
views.append(view.view)
if 'wall' in problem.map:
for wall in problem.map['wall']:
if wall not in views:
cimage = ws.add_image()
cimage.add_feature(wall, effect=False, view = True)
else:
logging.warning('There are no walls around! Check your task!!!')
sys.exit(1)
# Ground predicates and actions
if not exp_signs:
ut._ground_predicates(problem.domain['predicates'], signs)
ut._ground_actions(problem.domain['actions'], obj_means, problem.constraints, signs, agents, agent_type)
else:
#TODO check if new action or predicate variation appeared in new task
for pred in problem.domain['predicates'].copy():
if pred in exp_signs:
signs[pred] = exp_signs[pred]
problem.domain['predicates'].pop(pred)
for act in problem.domain['actions'].copy():
if act in exp_signs:
signs[act] = exp_signs[act]
problem.domain['actions'].pop(act)
ut._ground_predicates(problem.domain['predicates'], signs)
ut._ground_actions(problem.domain['actions'], obj_means, problem.constraints, signs, agents, agent_type)
#Copy all experience subplans and plans
for sname, sign in exp_signs.items():
if sname not in signs:
signs[sname] = sign
# Define start situation
maps = {}
if backward:
maps[0] = problem.goal_state
else:
maps[0] = problem.initial_state
ms = maps[0].pop('map-size')
walls = maps[0].pop('wall')
static_map = {'map-size': ms, 'wall': walls}
# Create maps and situations for planning agents
regions_struct = ut.get_struct()
additions = []
additions.extend([maps, regions_struct, static_map])
cells = {}
agent_state = {}
for agent in agents:
region_map, cell_map_start, cell_location, near_loc, cell_coords, size, cl_lv_init = ut.signs_markup(init_state, static_map,
agent)
agent_state_start = ut.state_prediction(signs[agent], init_state, signs)
start_situation = ut.define_situation('*start-sit*-'+agent, cell_map_start, problem.initial_state['conditions'], agent_state_start, signs)
start_map = ut.define_map('*start-map*-'+agent, region_map, cell_location, near_loc, regions_struct, signs)
ut.state_fixation(start_situation, cell_coords, signs, 'cell')
# Define goal situation
region_map, cell_map_goal, cell_location, near_loc, cell_coords, size, cl_lv_goal = ut.signs_markup(go_state, static_map,
agent)
agent_state_finish = ut.state_prediction(signs[agent], go_state, signs)
goal_situation = ut.define_situation('*goal-sit*-'+agent, cell_map_goal, problem.goal_state['conditions'], agent_state_finish, signs)
goal_map = ut.define_map('*goal-map*-'+agent, region_map, cell_location, near_loc, regions_struct, signs)
ut.state_fixation(goal_situation, cell_coords, signs, 'cell')
#fixation map
map_size = ut.scale(ms)
rmap = [0, 0, map_size[0], map_size[1]]
region_location, _ = ut.locater('region-', rmap, initial_state['objects'], walls)
ut.state_fixation(start_map, region_location, signs, 'region')
ut.signify_connection(signs)
if agent == 'I':
additions[0][0][plagent]['cl_lv_init'] = cl_lv_init
additions[0][0][plagent]['cl_lv_goal'] = cl_lv_goal
else:
additions[0][0][agent]['cl_lv_init'] = cl_lv_init
additions[0][0][agent]['cl_lv_goal'] = cl_lv_goal
if backward:
cell_map_goal['cell-4'] = {plagent}
cells[agent] = {0:cell_map_goal}
else:
cell_map_start['cell-4'] = {plagent}
cells[agent] = {0: cell_map_start}
agent_state[agent] = {'start-sit':start_situation.sign, 'goal-sit': goal_situation.sign, 'start-map':start_map.sign,
'goal-map': goal_map.sign}
additions.insert(2, cells)
return SpTask(problem.name, signs, agent_state, additions, problem.initial_state,
{key:value for key, value in problem.goal_state.items() if key not in static_map}, static_map, plagent)
def get_spatial_sit_blocks(self, action, cur_sit, prev_size, prev_cl):
if action[3].name == 'I':
ag_name = self.name
else:
ag_name = action[3].name
new_sit = deepcopy(cur_sit)
ag_pos = new_sit['objects'][ag_name]
ag_activity = new_sit[ag_name]['activity']
ground_block = None
clear_block = None
move_block = None
# we need to know object's coords in effect
for el in action[2].effect:
el_signs = el.get_signs()
if (action[1] == 'pick-up' or action[1] == 'unstack')\
and 'holding' in [sign.name for sign in el_signs]:
move_block = [
sign for sign in el_signs
if sign.name != 'holding' and sign.name != ag_name
][0]
break
elif (action[1] == 'stack'
or action[1] == 'put-down') and 'clear' in [
sign.name for sign in el_signs
]:
clear_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'stack':
for el in action[2].cause:
el_signs = el.get_signs()
if 'clear' in [sign.name for sign in el_signs]:
ground_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'unstack':
for el in action[2].effect:
el_signs = el.get_signs()
if 'clear' in [sign.name for sign in el_signs]:
ground_block = [
sign for sign in el_signs if sign.name != 'clear'
][0]
break
if action[1] == 'put-down' or action[1] == 'stack':
obj = clear_block
else:
obj = move_block
def get_pos(ag_pos, obj_pos, borders):
"""
Thus function was implemented to foresee the agent's position
after it will manipulate with object.
"""
def check_borders(x, y, borders):
for border in borders:
if (border[0] <= x <= border[2]) and (border[1] <= y <=
border[3]):
return False
return True
def get_random_side(obj_pos, ag_pos):
"""
get the closest path without borders. To make it random - delete path.
"""
csides = ['ar', 'br', 'r', 'bl', 'al', 'l', 'a', 'b']
path = []
while csides:
side = csides[0]
x, y, orient = getattr(self, side)(obj_pos, ag_pos)
if check_borders(x, y, borders):
p = math.sqrt((x - ag_pos['x'])**2 +
(y - ag_pos['y'])**2)
path.append(((x, y, orient), p))
csides.remove(side)
else:
csides.remove(side)
min_dist = None
for place, dist in path:
if not min_dist: min_dist = dist
if min_dist > dist:
min_dist = dist
return [place for place, path in path if path == min_dist][0]
if ag_pos['x'] < obj_pos['x']:
if ag_pos['y'] < obj_pos['y'] - ag_pos['r']:
x, y, orient = self.br(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
elif ag_pos['y'] > obj_pos['y'] + ag_pos['r']:
x, y, orient = self.ar(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
else:
x, y, orient = self.r(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
elif ag_pos['x'] > obj_pos['x']:
if ag_pos['y'] < obj_pos['y'] - ag_pos['r']:
x, y, orient = self.bl(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
elif ag_pos['y'] > obj_pos['y'] + ag_pos['r']:
x, y, orient = self.al(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
else:
x, y, orient = self.l(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
else:
if ag_pos['y'] < obj_pos['y']:
x, y, orient = self.b(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
else:
x, y, orient = self.a(obj_pos, ag_pos)
if check_borders(x, y, borders):
return x, y, orient
else:
return get_random_side(obj_pos, ag_pos)
# todo change to set of applicable poses
size = None
cl_lv = 0
if action[1] == 'pick-up' or action[1] == 'unstack':
obj_pos = new_sit['objects'][obj.name]
x, y, orient = get_pos(ag_pos, obj_pos,
self.task.additions[3]['wall'])
ag_pos['x'] = x
ag_pos['y'] = y
# check old size, on which agent can perform an action
_, _, _, _, _, size, cl_lv = signs_markup(new_sit,
self.task.additions[3],
ag_name)
new_sit['objects'].pop(obj.name)
for pred, signif in new_sit[ag_name].copy().items():
if pred == 'orientation':
new_sit[ag_name].pop(pred)
new_sit[ag_name][pred] = orient
elif pred == 'handempty':
new_sit[ag_name].pop(pred)
new_sit[ag_name]['holding'] = {
'cause': [ag_name, move_block.name],
'effect': []
}
elif action[1] == 'put-down':
new_sit['objects'][obj.name] = {}
obj_pos = new_sit['objects'][obj.name]
obj_pos['x'] = ag_pos['x'] + 2 * ag_pos['r'] + ag_activity // 2
obj_pos['y'] = ag_pos['y'] + 2 * ag_pos['r'] + ag_activity // 2
obj_pos['r'] = self.task.additions[0][0]['objects'][obj.name]['r']
elif action[1] == 'stack':
ground_coords = new_sit['objects'][ground_block.name]
x, y, orient = get_pos(ag_pos, ground_coords,
self.task.additions[3]['wall'])
# if we need to move, or stay on prev position
if math.sqrt((ag_pos['y'] - y)**2 +
(ag_pos['x'] - x)**2) >= ag_pos['r'] * 2:
ag_pos['x'] = x
ag_pos['y'] = y
new_sit['objects'][obj.name] = {}
obj_pos = new_sit['objects'][obj.name]
obj_pos['x'] = ground_coords['x']
obj_pos['y'] = ground_coords['y']
obj_pos['r'] = self.task.additions[0][0]['objects'][obj.name]['r']
for pred, signif in new_sit[ag_name].copy().items():
if pred == 'orientation':
new_sit[ag_name].pop(pred)
new_sit[ag_name][pred] = orient
elif pred == 'holding':
new_sit[ag_name].pop(pred)
new_sit[ag_name]['handempty'] = {'cause': [], 'effect': []}
region_map, cell_map_pddl, cell_location, near_loc, cell_coords, size, cl_lv = signs_markup(
new_sit, self.task.additions[3], ag_name, size=size, cl_lv=cl_lv)
agent_state_action = state_prediction(self.task.signs[ag_name],
new_sit, self.task.signs)
conditions_new = get_conditions(new_sit, action, obj.name,
ground_block)
new_sit['conditions'] = conditions_new
action_situation = define_situation('*goal-sit-*-' + action[1],
cell_map_pddl, conditions_new,
agent_state_action,
self.task.signs)
action_map = define_map('*goal-map-*-' + action[1], region_map,
cell_location, near_loc,
self.task.additions[1], self.task.signs)
state_fixation(action_situation, cell_coords, self.task.signs, 'cell')
return action_situation, action_map, cl_lv, new_sit, size