def _rand_walls(self):
wall_bids = {name: self.model.body_name2id(name) for name in ['wall_'+dir for dir in 'nesw']}
window_gid = self.model.geom_name2id('west_window')
#floor_gid = self.model.geom_name2id('floor')
WA_X = Range(-0.2, 0.2)
WA_Y = Range(-0.2, 0.2)
WA_Z = Range(-0.1, 0.1)
WA_R3D = Range3D(WA_X, WA_Y, WA_Z)
WI_X = Range(-0.1, 0.1)
WI_Y = Range(0, 0)
WI_Z = Range(-0.5, 0.5)
WI_R3D = Range3D(WI_X, WI_Y, WI_Z)
R = Range(0,0)
P = Range(-10,10)
Y = Range(0,0)
RPY_R = Range3D(R,P,Y)
#self.model.geom_quat[floor_gid] = jitter_quat(self.START_GEOM_QUAT[floor_gid], 0.01)
#self.model.geom_pos[floor_gid] = self.START_GEOM_POS[floor_gid] + [0,0,sample(-0.1,0.1)
self.model.geom_quat[window_gid] = sample_quat(RPY_R)
#self.model.geom_quat[window_gid] = jitter_quat(self.START_GEOM_QUAT[window_gid], 0.01)
self.model.geom_pos[window_gid] = self.START_GEOM_POS[window_gid] + sample_xyz(WI_R3D)
for name in wall_bids:
gid = wall_bids[name]
self.model.body_quat[gid] = jitter_quat(self.START_BODY_QUAT[gid], 0.01)
self.model.body_pos[gid] = self.START_BODY_POS[gid] + sample_xyz(WA_R3D)
def _rand_distract(self):
PREFIX = 'distract'
geom_names = [
name for name in self.model.geom_names if name.startswith(PREFIX)
]
# Size range
SX = Range(0.01, 0.5)
SY = Range(0.01, 0.9)
SZ = Range(0.01, 0.5)
S3D = Range3D(SX, SY, SZ)
# Back range
B_PX = Range(-0.5, 2)
B_PY = Range(-1.5, 2)
B_PZ = Range(0, 3)
B_P3D = Range3D(B_PX, B_PY, B_PZ)
# Front range
F_PX = Range(-2, -0.5)
F_PY = Range(-2, 1)
F_PZ = Range(0, 0.5)
F_P3D = Range3D(F_PX, F_PY, F_PZ)
for name in geom_names:
gid = self.model.geom_name2id(name)
range = B_P3D if np.random.binomial(1, 0.5) else F_P3D
self.model.geom_pos[gid] = sample_xyz(range)
self.model.geom_quat[gid] = random_quat()
self.model.geom_size[gid] = sample_xyz(S3D, mode='logspace')
self.model.geom_type[gid] = sample_geom_type()
self.model.geom_rgba[gid][-1] = np.random.binomial(1, 0.5)
def _rand_object(self):
obj_gid = self.model.geom_name2id('object')
table_gid = self.model.geom_name2id('object_table')
table_bid = self.model.body_name2id('object_table')
xval = self.model.geom_size[table_gid][0] #- self.model.geom_size[obj_gid][0]
yval = self.model.geom_size[table_gid][1] #- self.model.geom_size[obj_gid][1]
O_X = Range(-xval, xval)
O_Y = Range(-yval, yval)
O_Z = Range(0, 0)
O_R3D = Range3D(O_X, O_Y, O_Z)
self.model.geom_pos[obj_gid] = self.START_GEOM_POS[obj_gid] + sample_xyz(O_R3D)
def _next_camera(self):
"""Randomize pos, orientation, and fov of camera
real camera pos is -1.75, 0, 1.62
FOVY:
Kinect2 is 53.8
ASUS is 45
https://www.asus.com/us/3D-Sensor/Xtion_PRO_LIVE/specifications/
http://smeenk.com/kinect-field-of-view-comparison/
"""
# Params
FOVY_R = Range(40, 50)
#X = Range(-3, -1)
#Y = Range(-1, 3)
#Z = Range(1, 2)
#C_R3D = Range3D(X, Y, Z)
#cam_pos = sample_xyz(C_R3D)
#L_R3D = rto3d([-0.1, 0.1])
C_R3D = Range3D([-0.07, 0.07], [-0.07, 0.07], [-0.07, 0.07])
ANG3 = Range3D([-3, 3], [-3, 3], [-3, 3])
# Look approximately at the robot, but then randomize the orientation around that
# linearly interpolate to the next camera every K steps
K = 5
goal_cam_pos = self._cam_choices[(self._cam_step // K) + 1]
offset = goal_cam_pos - self._curr_cam_pos
offset *= (self._cam_step % K) / K
self._curr_cam_pos += offset
cam_pos = self._curr_cam_pos
self._cam_step += 1
# cam_pos = cam_choices[np.random.choice(len(cam_choices))]
# cam_pos = get_real_cam_pos(FLAGS.real_data_path)
target_id = self.model.body_name2id(FLAGS.look_at)
cam_off = 0 #sample_xyz(L_R3D)
target_off = 0 #sample_xyz(L_R3D)
quat = look_at(cam_pos + cam_off,
self.sim.data.body_xpos[target_id] + target_off)
quat = jitter_angle(quat, ANG3)
#quat = jitter_quat(quat, 0.01)
cam_pos += sample_xyz(C_R3D)
self.cam_modder.set_quat('camera1', quat)
self.cam_modder.set_pos('camera1', cam_pos)
self.cam_modder.set_fovy('camera1', 60) # hard code to wide fovy
def _rand_object(self):
obj_gid = self.sim.model.geom_name2id('object')
obj_bid = self.sim.model.geom_name2id('object')
table_gid = self.model.geom_name2id('object_table')
table_bid = self.model.body_name2id('object_table')
obj_pose = self.start_obj_pose.copy()
xval = self.model.geom_size[table_gid][
0] #- self.model.geom_size[obj_gid][0]
yval = self.model.geom_size[table_gid][
1] #- self.model.geom_size[obj_gid][1]
O_X = Range(-xval, xval)
O_Y = Range(-yval, yval)
O_Z = Range(0, 0)
O_R3D = Range3D(O_X, O_Y, O_Z)
newpos = obj_pose[:3] + sample_xyz(O_R3D)
newquat = jitter_quat(obj_pose[3:], 0.1)
obj_pose[:3] = newpos
obj_pose[3:] = newquat
self.sim.data.set_joint_qpos('object:joint', obj_pose)
def _rand_lights(self):
"""Randomize pos, direction, and lights"""
# light stuff
#X = Range(-1.5, 1.5)
#Y = Range(-1.2, 1.2)
#Z = Range(0, 2.8)
X = Range(-1.5, -0.5)
Y = Range(-0.6, 0.6)
Z = Range(1.0, 1.5)
LIGHT_R3D = Range3D(X, Y, Z)
LIGHT_UNIF = Range3D(Range(0, 1), Range(0, 1), Range(0, 1))
# TODO: also try not altering the light dirs and just keeping them at like -1, or [0, -0.15, -1.0]
for i, name in enumerate(self.model.light_names):
lid = self.model.light_name2id(name)
# random sample 80% of any given light being on
if lid != 0:
self.light_modder.set_active(name, sample([0, 1]) < 0.8)
self.light_modder.set_dir(name, sample_light_dir())
self.light_modder.set_pos(name, sample_xyz(LIGHT_R3D))
#self.light_modder.set_dir(name, sample_xyz(rto3d([-1,1])))
#self.light_modder.set_specular(name, sample_xyz(LIGHT_UNIF))
#self.light_modder.set_diffuse(name, sample_xyz(LIGHT_UNIF))
#self.light_modder.set_ambient(name, sample_xyz(LIGHT_UNIF))
spec = np.array([sample(Range(0.5, 1))] * 3)
diffuse = np.array([sample(Range(0.5, 1))] * 3)
ambient = np.array([sample(Range(0.5, 1))] * 3)
self.light_modder.set_specular(name, spec)
self.light_modder.set_diffuse(name, diffuse)
self.light_modder.set_ambient(name, ambient)
#self.model.light_directional[lid] = sample([0,1]) < 0.2
self.model.light_castshadow[lid] = sample([0, 1]) < 0.5
def _rand_camera(self):
"""Randomize pos, orientation, and fov of camera
FOVY:
Kinect2 is 53.8
ASUS is 45
https://www.asus.com/us/3D-Sensor/Xtion_PRO_LIVE/specifications/
http://smeenk.com/kinect-field-of-view-comparison/
"""
# Params
FOVY_R = Range(40, 50)
#X = Range(-3, -1)
#Y = Range(-1, 3)
#Z = Range(1, 2)
#C_R3D = Range3D(X, Y, Z)
#cam_pos = sample_xyz(C_R3D)
#L_R3D = rto3d([-0.1, 0.1])
C_R3D = Range3D([-0.05,0.05], [-0.05,0.05], [-0.05,0.05])
ANG3 = Range3D([-3,3], [-3,3], [-3,3])
# Look approximately at the robot, but then randomize the orientation around that
cam_pos = get_real_cam_pos(FLAGS.real_data_path)
target_id = self.model.body_name2id(FLAGS.look_at)
cam_off = 0 #sample_xyz(L_R3D)
target_off = 0 #sample_xyz(L_R3D)
quat = look_at(cam_pos+cam_off, self.sim.data.body_xpos[target_id]+target_off)
quat = jitter_angle(quat, ANG3)
#quat = jitter_quat(quat, 0.01)
cam_pos += sample_xyz(C_R3D)
self.cam_modder.set_quat('camera1', quat)
self.cam_modder.set_pos('camera1', cam_pos)
self.cam_modder.set_fovy('camera1', sample(FOVY_R))