def two_tables_through_door(env, obj_length, n=7): # Previously 4, 8
env.Load(ENVIRONMENTS_DIR + 'two_tables'+str(obj_length)+'.xml')
#set_default_robot_config(env.GetRobots()[0])
table_names = filter(lambda name: 'table' in name, [get_name(body) for body in env.GetBodies() if not body.IsRobot()])
objects = []
# objects.append(env.GetKinBody("ObjToG"))
goal_regions = {}
for i in range(2*n):
objects.append(box_body(env, .07, .07, .2, name='red'+str(i+1), color=RED))
for i in range(n):
name = 'green'+str(i+1)
objects.append(box_body(env, .07, .07, .2, name=name, color=GREEN))
goal_regions[name] = 'table1'
for i in range(n):
name = 'blue'+str(i+1)
objects.append(box_body(env, .07, .07, .2, name=name, color=BLUE))
goal_regions[name] = 'table3'
object_names = [get_name(body) for body in objects]
for obj in randomize(objects):
randomly_place_body(env, obj, ['table2', 'table4'])
return ManipulationProblem(None,
object_names=object_names, table_names=table_names,
goal_regions=goal_regions)
def grow(self, goal, iterations=50, store=ts.PATH, max_tree_size=500):
if goal in self: self[goal].retrace()
if self.collision(goal): return None
nodes1, new_nodes1 = list(take(randomize(self.nodes.values()), max_tree_size)), []
nodes2, new_nodes2 = [], [TreeNode(goal)]
for _ in irange(iterations):
if len(nodes1) + len(new_nodes1) > len(nodes2) + len(new_nodes2):
nodes1, nodes2 = nodes2, nodes1
new_nodes1, new_nodes2 = new_nodes2, new_nodes1
s = self.sample()
last1 = argmin(lambda n: self.distance(n.config, s), nodes1 + new_nodes1)
for q in self.extend(last1.config, s):
if self.collision(q): break
last1 = TreeNode(q, parent=last1)
new_nodes1.append(last1)
last2 = argmin(lambda n: self.distance(n.config, last1.config), nodes2 + new_nodes2)
for q in self.extend(last2.config, last1.config):
if self.collision(q): break
last2 = TreeNode(q, parent=last2)
new_nodes2.append(last2)
else:
if len(nodes1) == 0:
nodes1, nodes2 = nodes2, nodes1
new_nodes1, new_nodes2 = new_nodes2, new_nodes1
last1, last2 = last2, last1
path1, path2 = last1.retrace(), last2.retrace()[:-1][::-1]
for p, n in pairs(path2): n.parent = p
if len(path2) == 0: # TODO - still some kind of circular error
for n in new_nodes2:
if n.parent == last2:
n.parent = path1[-1]
else:
path2[0].parent = path1[-1]
path = path1 + path2
if store in [ts.ALL, ts.SUCCESS]:
self.add(*(new_nodes1 + new_nodes2[:-1]))
elif store == ts.PATH:
new_nodes_set = set(new_nodes1 + new_nodes2[:-1])
self.add(*[n for n in path if n in new_nodes_set])
return path
if store == ts.ALL:
self.add(*new_nodes1)
return None
def shelf_arrangement(env): # (Dealing with Difficult Instances of Object Rearrangment)
env.Load(ENVIRONMENTS_DIR + 'empty.xml')
#m, n = 2, 10
m, n = 2, 4
box_dims = (.07, .07, .2)
#separation = (.08, .08)
separation = (.15, .15)
length = m*(box_dims[0] + separation[0])
width = n*(box_dims[1] + separation[1])
height = .7
table = box_body(env, length, width, height, name='table', color=get_color('tan1'))
set_point(table, (1.75, 0, 0))
env.Add(table)
# TODO - place walls and/or a roof to make more similar to pebble graph people
objects = []
goal_poses = {}
z = get_point(table)[2] + height + BODY_PLACEMENT_Z_OFFSET
for i in range(m):
x = get_point(table)[0] - length/2 + (i+.5)*(box_dims[0] + separation[0])
row_color = np.zeros(4)
row_color[2-i] = 1.
for j in range(n):
y = get_point(table)[1] - width/2 + (j+.5)*(box_dims[1] + separation[1])
name = 'block%d-%d'%(i, j)
color = row_color + float(j)/(n-1)*np.array([1, 0, 0, 0])
goal_poses[name] = Pose(pose_from_quat_point(unit_quat(), np.array([x, y, z])))
objects.append(box_body(env, *box_dims, name=name, color=color))
object_names = [get_name(body) for body in objects]
print object_names
for obj in randomize(objects):
randomly_place_body(env, obj, [get_name(table)])
return ManipulationProblem(None,
object_names=object_names, table_names=[get_name(table)],
goal_poses=goal_poses)
def grow(self, goal_sample, iterations=50, goal_probability=.2, store=ts.PATH, max_tree_size=500):
if not callable(goal_sample): goal_sample = lambda: goal_sample
nodes, new_nodes = list(take(randomize(self.nodes.values()), max_tree_size)), []
for i in irange(iterations):
goal = random() < goal_probability or i == 0
s = goal_sample() if goal else self.sample()
last = argmin(lambda n: self.distance(n.config, s), nodes + new_nodes)
for q in self.extend(last.config, s):
if self.collision(q): break
last = TreeNode(q, parent=last)
new_nodes.append(last)
else:
if goal:
path = last.retrace()
if store in [ts.ALL, ts.SUCCESS]:
self.add(*new_nodes)
elif store == ts.PATH:
new_nodes_set = set(new_nodes)
self.add(*[n for n in path if n in new_nodes_set])
return path
if store == ts.ALL:
self.add(*new_nodes)
return None
def grid_arrangement(env): # (Dealing with Difficult Instances of Object Rearrangment)
# env.Load(ENVIRONMENTS_DIR + 'empty.xml')
env.Load(ENVIRONMENTS_DIR + 'regrasp_one_table.xml') # the environment for HBf
pb, bb = place_body, box_body
"""
pb(env, bb(env, .3, .05, .3, name='obst1', color=GREY), (1.65, .075, 0), 'table1')
pb(env, bb(env, .3, .05, .3, name='obst2', color=GREY), (1.65, .425, 0), 'table1')
pb(env, bb(env, .05, .4, .3, name='obst3', color=GREY), (1.825, .25, 0), 'table1')
"""
pb(env, bb(env, .3, .05, .3, name='obst4', color=GREY), (1.65, -.125, 0), 'table1')
pb(env, bb(env, .3, .05, .3, name='obst5', color=GREY), (1.65, -.375, 0), 'table1')
pb(env, bb(env, .05, .3, .3, name='obst6', color=GREY), (1.825, -.25, 0), 'table1')
obstacle_names = [str(body.GetName()) for body in env.GetBodies() if not body.IsRobot()]
table_names = ['table1', 'table2']
"""
pb(env, bb(env, .03, .1, .2, name='green', color=GREEN), (1.55, 0.25, 0), 'table1')
pb(env, bb(env, .03, .1, .2, name='blue', color=BLUE), (1.5, 0.25, 0), 'table1')
pb(env, bb(env, .05, .05, .1, name='red1', color=RED), (.1, -1.8, PI/16), 'table2')
pb(env, bb(env, .15, .05, .15, name='red2', color=RED), (-.4, -1.95, PI/5), 'table2')
pb(env, bb(env, .07, .07, .07, name='red3', color=RED), (.5, -1.9, PI/3), 'table2')
pb(env, bb(env, .1, .1, .25, name='red4', color=RED), (1.9, -0.55, PI/7), 'table1')
"""
set_default_robot_config(env.GetRobots()[0])
#m, n = 2, 10
m, n = 2, 10
box_dims = (.12, .04, .08)
#separation = (.08, .08)
separation = (.12, .12)
#separation = (.16, .16)
length = m*(box_dims[0] + separation[0])
width = n*(box_dims[1] + separation[1])
height = .7
# table = box_body(env, length, width, height, name='table', color=get_color('tan1'))
# set_point(table, (1.75, 0, 0))
# env.Add(table)
table = env.GetKinBody('table1')
objects = []
goal_poses = {}
z = get_point(table)[2] + height + BODY_PLACEMENT_Z_OFFSET
for i in range(m):
x = get_point(table)[0] - length/2 + (i+.5)*(box_dims[0] + separation[0])
row_color = np.zeros(4)
row_color[2-i] = 1.
for j in range(n):
y = get_point(table)[1] - width/2 + (j+.5)*(box_dims[1] + separation[1])
name = 'block%d-%d'%(i, j)
# if i==0 and j==0:
# color = np.array([1,0,0,0])
# box_dims = (.12, .06, .08)
# else:
color = row_color + float(j)/(n-1)*np.array([1, 0, 0, 0])
box_dims = (.12, .04, .1)
goal_poses[name] = Pose(pose_from_quat_point(unit_quat(), np.array([x, y, z])))
objects.append(box_body(env, *box_dims, name=name, color=color))
object_names = [get_name(body) for body in objects]
object_names.append('ObjToG')
objects.append(env.GetKinBody('ObjToG'))
for obj in randomize(objects):
#randomly_place_body(env, obj, [get_name(table)])
randomly_place_body(env, obj, ['table1'])
return ManipulationProblem(None,
object_names=object_names, table_names=[get_name(table)],
goal_poses=goal_poses)