def two_tables(env, n=2):
assert not REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + '2tables.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()])
#m = 4*n
objects = []
goal_regions = {}
#for i in range(4*m):
for i in range(10*n):
objects.append(box_body(env, .07, .07, .2, name='red'+str(i+1), color=RED))
#for i in range(n):
for i in range(1):
name = 'blue'+str(i+1)
objects.append(box_body(env, .07, .07, .2, name=name, color=BLUE))
goal_regions[name] = 'table2'
object_names = [get_name(body) for body in objects]
for obj in randomize(objects):
randomly_place_body(env, obj, ['table1'])
return ManipulationProblem(None,
object_names=object_names, table_names=table_names,
goal_regions=goal_regions)
def separate(env, n=7): # Previously 4, 8
assert not REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + 'tables.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 = []
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 two_tables_through_door(env, obj_length, n=7): # Previously 4, 8
env.Load(ENVIRONMENTS_DIR + 'two_tables'+str(obj_length)+'.xml')
#env.Load(ENVIRONMENTS_DIR + 'two_tables'+'.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'])
# randomly_place_body(env, obj, ['table2'])
return ManipulationProblem(None,
object_names=object_names, table_names=table_names,
goal_regions=goal_regions)
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 move_several(env, n):
assert not REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + 'empty.xml')
box_dims = (.07, .07, .2)
#separation = (.08, .08)
separation = (.10, .10)
length = math.sqrt(n+1)*(box_dims[0] + separation[0])
width = math.sqrt(n+1)*(box_dims[1] + separation[1])
height = .7
table1 = box_body(env, length, width, height, name='table1', color=get_color('tan1'))
set_point(table1, (0, 0, 0))
env.Add(table1)
table2 = box_body(env, length, width, height, name='table2', color=get_color('tan1'))
set_point(table2, (1.5, 0, 0))
env.Add(table2)
robot = env.GetRobots()[0]
set_default_robot_config(robot)
set_base_values(robot, (-1.5, 0, 0))
# TODO - place walls and/or a roof to make more similar to pebble graph people
objects = []
goal_regions = {}
obj = box_body(env, .07, .07, .2, name='blue', color=BLUE)
set_point(obj, (0, 0, height + BODY_PLACEMENT_Z_OFFSET))
objects.append(obj)
goal_regions[get_name(obj)] = get_name(table2)
env.Add(obj)
for i in range(n):
objects.append(box_body(env, .07, .07, .2, name='red'+str(i+1), color=RED))
for obj in randomize(objects[1:]):
randomly_place_body(env, obj, [get_name(table1)])
return ManipulationProblem(None,
object_names=[get_name(body) for body in objects], table_names=[get_name(table) for table in [table1, table2]],
goal_regions=goal_regions)
def grid_arrangement(env, m, n): # (Dealing with Difficult Instances of Object Rearrangment)
assert REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + 'empty.xml')
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))
set_point(table, (0, 0, 0))
env.Add(table)
robot = env.GetRobots()[0]
set_default_robot_config(robot)
set_base_values(robot, (-1.5, 0, 0))
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]
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 dantam(env): # (Incremental Task and Motion Planning: A Constraint-Based Approach)
assert REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + 'empty.xml')
set_default_robot_config(env.GetRobots()[0])
set_point(env.GetRobots()[0], (-1.5, 0, 0))
m, n = 3, 3
#m, n = 5, 5
n_obj = 8
side_dim = .07
height_dim = .1
box_dims = (side_dim, side_dim, height_dim)
separation = (side_dim, side_dim)
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, (0, 0, 0))
env.Add(table)
pose_indices = list(product(range(m), range(n)))
colors = {}
for r, c in pose_indices:
color = np.zeros(4)
color[2-r] = 1.
colors[(r, c)] = color + float(c)/(n-1)*np.array([1, 0, 0, 0])
poses = {}
z = get_point(table)[2] + height + BODY_PLACEMENT_Z_OFFSET
for r, c in pose_indices:
x = get_point(table)[0] - length/2 + (r+.5)*(box_dims[0] + separation[0])
y = get_point(table)[1] - width/2 + (c+.5)*(box_dims[1] + separation[1])
poses[(r, c)] = Pose(pose_from_quat_point(unit_quat(), np.array([x, y, z])))
initial_indices = randomize(pose_indices[:])
initial_poses = {}
goal_poses = {}
for i, indices in enumerate(pose_indices[:n_obj]):
name = 'block%d-%d'%indices
color = colors[indices]
initial_poses[name] = poses[initial_indices[i]]
goal_poses[name] = poses[indices]
obj = box_body(env, *box_dims, name=name, color=color)
set_pose(obj, initial_poses[name].value)
env.Add(obj)
#for obj in randomize(objects):
# randomly_place_body(env, obj, [get_name(table)])
return ManipulationProblem(function_name(inspect.stack()),
object_names=initial_poses.keys(), table_names=[get_name(table)],
goal_poses=goal_poses,
initial_poses=initial_poses, known_poses=poses.values())
def separate(env, n=7): # Previously 4, 8
env.Load(ENVIRONMENTS_DIR + 'tables.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 = []
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'
objects.append(box_body(env, .07, .07, .2, name='black', color=BLACK))
object_names = [get_name(body) for body in objects]
robot = env.GetRobots()[0]
robot.SetActiveManipulator('leftarm')
print robot.GetActiveManipulator().GetLocalToolTransform()
grasps = {}
#for obj_name in object_names:
obj_name = 'black'
env.Add(objects[-1])
obj = env.GetKinBody(obj_name)
with obj:
obj.SetTransform(np.eye(4))
obj_grasps = get_grasps(env, robot, obj, GRASP_APPROACHES.SIDE, GRASP_TYPES.GRASP)
#obj_grasps = get_grasps(env, robot, obj, GRASP_APPROACHES.TOP, GRASP_TYPES.TOUCH) # TOP and SIDE are swapped
grasps[get_name(obj)] = obj_grasps
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,grasps=grasps)
def randomly_place_in_region(env, body, region):
if env.GetKinBody(get_name(body)) is None: env.Add(body)
for i in range(1000):
set_quat(body, quat_from_z_rot(uniform(0, 2 * PI)))
aabb = aabb_from_body(body)
cspace = region.cspace(aabb)
if cspace is None: continue
set_point(body, np.array([uniform(*cspace_range) for cspace_range in cspace] + [
region.z + aabb.extents()[2] + BODY_PLACEMENT_Z_OFFSET]) - aabb.pos() + get_point(body))
if not env.CheckCollision(body):
return get_body_xytheta(body)
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]
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 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 srivastava_table(env, n=INF):
env.Load(ENVIRONMENTS_DIR + '../srivastava/good_cluttered.dae')
set_default_robot_config(env.GetRobots()[0])
body_names = [get_name(body) for body in env.GetBodies() if not body.IsRobot()]
table_names = [body_name for body_name in body_names if 'table' in body_name]
dx = .5
for body_name in body_names:
body = env.GetKinBody(body_name)
set_point(body, get_point(body) + np.array([dx, 0, 0]))
objects = [env.GetKinBody(body_name) for body_name in body_names if body_name not in table_names]
for obj in objects: env.Remove(obj)
object_names = []
for obj in take(objects, n):
randomly_place_body(env, obj, table_names)
object_names.append(get_name(obj))
goal_holding = 'object1'
goal_config = 'initial' # None
return ManipulationProblem(None,
object_names=object_names, table_names=table_names,
goal_config=goal_config, goal_holding=goal_holding)
def srivastava_table(env, n=INF):
env.Load(ENVIRONMENTS_DIR + '../srivastava/good_cluttered.dae')
set_default_robot_config(env.GetRobots()[0])
body_names = [get_name(body) for body in env.GetBodies() if not body.IsRobot()]
table_names = [body_name for body_name in body_names if 'table' in body_name]
dx = .5
for body_name in body_names:
body = env.GetKinBody(body_name)
set_point(body, get_point(body) + np.array([dx, 0, 0]))
objects = [env.GetKinBody(body_name) for body_name in body_names if body_name not in table_names]
for obj in objects: env.Remove(obj)
object_names = []
for obj in take(objects, n):
randomly_place_body(env, obj, table_names)
object_names.append(get_name(obj))
goal_holding = 'object1'
goal_config = 'initial' # None
return ManipulationProblem(None,
object_names=object_names, table_names=table_names,
goal_config=goal_config, goal_holding=goal_holding)
def get_grasps(env, robot, body, grasp_approach, grasp_type):
Options = get_grasp_options(body, grasp_approach)
grasp_options = Options.default(grasp_type, body)
filename = GRASP_FILENAME%positive_hash(grasp_options) # TODO - get rid of the negative numbers
"""
print 'Creating', get_name(body), GRASP_TYPES.names[grasp_type], 'database' # TODO - move this to create_grasp_database
c_grasps = hacked_create_grasp_database(env, grasp_options)
o_grasps = load_grasp_database(robot,filename)
return c_grasps
#import pdb;pdb.set_trace()
"""
try:
return load_grasp_database(robot, filename)
except IOError:
print 'Creating', get_name(body), GRASP_TYPES.names[grasp_type], 'database' # TODO - move this to create_grasp_database
grasps = hacked_create_grasp_database(env, grasp_options)
#save_grasp_database(filename, grasps) # TODO - not saving right not because incomplete
return grasps
def gaussian_randomly_place_in_region(env, body, region, center, var):
if env.GetKinBody(get_name(body)) is None:
env.Add(body)
for i in range(1000):
xytheta = np.random.normal(center, var)
set_obj_xytheta(xytheta, body)
if not body_collision(env, body):
return xytheta
import pdb;pdb.set_trace()
for i in range(1000):
set_quat(body, quat_from_z_rot(uniform(0, 2 * PI)))
aabb = aabb_from_body(body)
cspace = region.cspace(aabb)
if cspace is None: continue
set_point(body, np.array([uniform(*cspace_range) for cspace_range in cspace] + [
region.z + aabb.extents()[2] + BODY_PLACEMENT_Z_OFFSET]) - aabb.pos() + get_point(body))
if not body_collision(env, body):
return get_body_xytheta(body)
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)
def get_body_name(self, geom_hash):
for body in self.env.GetBodies():
if geometry_hash(body) == geom_hash:
return get_name(body)
return None
def rearrangement_problem(env,obj_poses=None):
# import random
# seed = 50
# random.seed(seed)
# np.random.seed(seed)
MIN_DELTA = .01 # .01 | .02
ENV_FILENAME = ENVIRONMENTS_DIR+'/single_table.xml'
env.Load(ENV_FILENAME)
robot = env.GetRobots()[0]
TABLE_NAME = 'table1'
NUM_OBJECTS = 8
WIDTH = .05 # .07 | .1
TARGET_WIDTH = 0.07
OBJ_HEIGHT = 0.2
objects = [box_body(env, WIDTH, WIDTH, .2, name='obj%s'%i, \
color=(0, (i+.5)/NUM_OBJECTS, 0)) for i in range(NUM_OBJECTS)]
target_obj = box_body(env, TARGET_WIDTH, TARGET_WIDTH, .2, name='target_obj', \
color=(1, 1.5, 0))
#TODO: Place obstacle that prevent you to reach from top
OBST_X = 0
OBST_WIDTH = 1.
OBST_COLOR = (1, 0, 0)
OBST_HEIGHT = 0.4
OBST_TRANSPARENCY = .25
place_body(env, box_body(env, .4, .05, OBST_HEIGHT, name='obst1', color=OBST_COLOR, transparency=OBST_TRANSPARENCY),
(.0, OBST_X-(OBST_WIDTH-.05)/2, 0), TABLE_NAME)
place_body(env, box_body(env, .4, .05, OBST_HEIGHT, name='obst2', color=OBST_COLOR, transparency=OBST_TRANSPARENCY),
(.0, OBST_X+(OBST_WIDTH-.05)/2, 0), TABLE_NAME)
place_body(env, box_body(env, .05, OBST_WIDTH, OBST_HEIGHT, name='obst3', color=OBST_COLOR, transparency=OBST_TRANSPARENCY),
(.225, OBST_X, 0), TABLE_NAME)
# roof
OBST_Z = OBST_HEIGHT
place_xyz_body(env, box_body(env, .4, OBST_WIDTH, .01, name='obst4', color=OBST_COLOR, transparency=OBST_TRANSPARENCY),
(0, OBST_X, OBST_Z,0), TABLE_NAME)
# I think this can be done once and for all
REST_TORSO = [.15]
robot.SetDOFValues(REST_TORSO, [robot.GetJointIndex('torso_lift_joint')])
l_model = databases.linkstatistics.LinkStatisticsModel(robot)
if not l_model.load(): l_model.autogenerate()
l_model.setRobotWeights()
min_delta = 0.01
l_model.setRobotResolutions(xyzdelta=min_delta) # xyzdelta is the minimum Cartesian distance of an object
extrema = aabb_extrema(aabb_union([aabb_from_body(body) for body in env.GetBodies()])).T
robot.SetAffineTranslationLimits(*extrema)
X_PERCENT = 0.0
# define intial region for movable objects
init_region = create_region(env, 'init_region', ((-0.8, 0.8), (-0.7, 0.6)), TABLE_NAME, color=np.array((1, 0, 1, .5)))
#init_region.draw(env)
# define target object region
target_obj_region = create_region(env, 'target_obj_region', ((-0.2, 0.6), (-0.5,0.5)), \
TABLE_NAME, color=np.array((0, 0, 0, 0.9)))
target_obj_region.draw(env)
# place object
if obj_poses is not None:
for obj in objects:
set_pose(obj,obj_poses[get_name(obj)])
set_quat(obj, quat_from_z_rot(0))
if env.GetKinBody(get_name(obj)) is None: env.Add(obj)
set_pose( target_obj, obj_poses[get_name(target_obj)] )
set_quat( target_obj, quat_from_z_rot(0) )
if env.GetKinBody(get_name(target_obj)) is None: env.Add(target_obj)
else:
for obj in objects:
randomly_place_region(env, obj, init_region)
set_quat(obj, quat_from_z_rot(0))
randomly_place_region(env, target_obj, target_obj_region)
set_quat(target_obj, quat_from_z_rot(0))
object_names = [get_name(body) for body in objects] # (tothefront,totheback), (to_the_right,to_the_left)
set_xy(robot, -.75, 0)
# LEFT_ARM_CONFIG = HOLDING_LEFT_ARM
robot.SetDOFValues(REST_LEFT_ARM, robot.GetManipulator('leftarm').GetArmIndices())
robot.SetDOFValues(mirror_arm_config(REST_LEFT_ARM), robot.GetManipulator('rightarm').GetArmIndices())
grasps = {}
for obj_name in object_names:
obj = env.GetKinBody(obj_name)
obj_grasps = get_grasps(env, robot, obj, GRASP_APPROACHES.SIDE, GRASP_TYPES.TOUCH) \
+ get_grasps(env, robot, obj, GRASP_APPROACHES.TOP, GRASP_TYPES.TOUCH)
grasps[get_name(obj)] = obj_grasps
target_obj = env.GetKinBody('target_obj')
target_obj_grasps = get_grasps(env, robot, target_obj, GRASP_APPROACHES.TOP, GRASP_TYPES.TOUCH)+\
get_grasps(env, robot, target_obj, GRASP_APPROACHES.SIDE, GRASP_TYPES.TOUCH)
grasps['target_obj'] = target_obj_grasps
initial_poses={}
for obj in objects:
initial_poses[get_name(obj)] = get_pose(obj)
initial_poses[get_name(target_obj)] = get_pose(target_obj)
return ManipulationProblem(None,
object_names=object_names, table_names='table1',
goal_regions=init_region,grasps=grasps,
initial_poses = initial_poses)
def dantam_distract(env, n_obj): # (Incremental Task and Motion Planning: A Constraint-Based Approach)
assert REARRANGEMENT
env.Load(ENVIRONMENTS_DIR + 'empty.xml')
m, n = 3, 3
#m, n = 5, 5
side_dim = .07 # .05 | .07
height_dim = .1
box_dims = (side_dim, side_dim, height_dim)
separation = (side_dim, side_dim)
#separation = (side_dim/2, side_dim/2)
coordinates = list(product(range(m), range(n)))
assert n_obj <= len(coordinates)
obj_coordinates = sample(coordinates, n_obj)
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, (0, 0, 0))
env.Add(table)
robot = env.GetRobots()[0]
set_default_robot_config(robot)
set_base_values(robot, (-1.5, 0, 0))
#set_base_values(robot, (0, width/2 + .5, math.pi))
#set_base_values(robot, (.35, width/2 + .35, math.pi))
#set_base_values(robot, (.35, width/2 + .35, 3*math.pi/4))
poses = []
z = get_point(table)[2] + height + BODY_PLACEMENT_Z_OFFSET
for r in range(m):
row = []
x = get_point(table)[0] - length/2 + (r+.5)*(box_dims[0] + separation[0])
for c in range(n):
y = get_point(table)[1] - width/2 + (c+.5)*(box_dims[1] + separation[1])
row.append(Pose(pose_from_quat_point(unit_quat(), np.array([x, y, z]))))
poses.append(row)
initial_poses = {}
goal_poses = {}
# TODO - randomly assign goal poses here
for i, (r, c) in enumerate(obj_coordinates):
row_color = np.zeros(4)
row_color[2-r] = 1.
if i == 0:
name = 'goal%d-%d'%(r, c)
color = BLUE
goal_poses[name] = poses[m/2][n/2]
else:
name = 'block%d-%d'%(r, c)
color = RED
initial_poses[name] = poses[r][c]
obj = box_body(env, *box_dims, name=name, color=color)
set_pose(obj, poses[r][c].value)
env.Add(obj)
#for obj in randomize(objects):
# randomly_place_body(env, obj, [get_name(table)])
known_poses = list(flatten(poses))
#known_poses = list(set(flatten(poses)) - {poses[r][c] for r, c in obj_coordinates}) # TODO - put the initial poses here
return ManipulationProblem(function_name(inspect.stack()),
object_names=initial_poses.keys(), table_names=[get_name(table)],
goal_poses=goal_poses,
initial_poses=initial_poses, known_poses=known_poses)
def get_grasps(env, robot, body, grasp_approach, grasp_type):
Options = get_grasp_options(body, grasp_approach)
grasp_options = Options.default(grasp_type, body)
print 'Creating', get_name(body), GRASP_TYPES.names[
grasp_type], 'database' # TODO - move this to create_grasp_database
return hacked_create_grasp_database(env, grasp_options)
def robot_collision(oracle, body_name=None, check_self=True):
if body_name is None:
return collision(oracle, get_name(oracle.robot)) or (
not check_self and self_collision(oracle, get_name(oracle.robot)))
return collision(oracle, get_name(oracle.robot), body_name)
