def __init__(self, filepath, random_params={}, gpu_render=False, gui=False, display_data=False):
self.model = load_model_from_path(filepath)
self.sim = MjSim(self.model)
self.filepath = filepath
self.gui = gui
self.display_data = display_data
# Take the default random params and update anything we need
self.RANDOM_PARAMS = {}
self.RANDOM_PARAMS.update(random_params)
if gpu_render:
self.viewer = MjViewer(self.sim)
else:
self.viewer = None
# Get start state of params to slightly jitter later
self.START_GEOM_POS = self.model.geom_pos.copy()
self.START_GEOM_SIZE = self.model.geom_size.copy()
self.START_GEOM_QUAT = self.model.geom_quat.copy()
self.START_BODY_POS = self.model.body_pos.copy()
self.START_BODY_QUAT = self.model.body_quat.copy()
self.START_MATID = self.model.geom_matid.copy()
#self.FLOOR_OFFSET = self.model.body_pos[self.model.body_name2id('floor')]
self.tex_modder = TextureModder(self.sim)
self.tex_modder.whiten_materials() # ensures materials won't impact colors
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
def _re_init(self, xml):
# TODO: Now, likely needs rank
randomized_path = os.path.join(self.xml_dir, "randomizedgen3.xml")
with open(randomized_path, 'wb') as fp:
fp.write(xml.encode())
fp.flush()
try:
self.model = mujoco_py.load_model_from_path(randomized_path)
except:
print("Unable to load the xml file")
self.sim = mujoco_py.MjSim(self.model, nsubsteps=self.n_substeps)
self.modder = TextureModder(self.sim)
self.mat_modder = MaterialModder(self.sim)
self.light_modder = LightModder(self.sim)
self.camera_modder = CameraModder(self.sim)
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self._env_setup(initial_qpos=self.initial_qpos)
self.initial_state = copy.deepcopy(self.sim.get_state())
if self.viewer:
self.viewer.update_sim(self.sim)
def __init__(self, model_path, initial_qpos, n_actions, n_substeps):
if model_path.startswith('/'):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), 'assets',
model_path)
if not os.path.exists(fullpath):
raise IOError('File {} does not exist'.format(fullpath))
model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(model, nsubsteps=n_substeps)
#self.viewer = None # comment when using "human"
self.viewer = mujoco_py.MjViewer(
self.sim) #comment when using "rgb_array"
self._viewers = {}
self.modder = TextureModder(self.sim)
self.visual_randomize = True
self.mat_modder = MaterialModder(self.sim)
self.light_modder = LightModder(self.sim)
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self.visual_data_recording = True
self._index = 0
self._label_matrix = []
self.seed()
self._env_setup(initial_qpos=initial_qpos)
self.initial_state = copy.deepcopy(self.sim.get_state())
self.goal = self._sample_goal()
obs = self._get_obs()
self.action_space = spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32')
self.observation_space = spaces.Dict(
dict(
desired_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
achieved_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
observation=spaces.Box(-np.inf,
np.inf,
shape=obs['observation'].shape,
dtype='float32'),
))
if self.viewer:
self._viewer_setup()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Get start state of params to slightly jitter later
self.start_geo_size = self.model.geom_size.copy()
self.start_geom_quat = self.model.geom_quat.copy()
self.start_body_pos = self.model.body_pos.copy()
self.start_body_quat = self.model.body_quat.copy()
self.start_matid = self.model.geom_matid.copy()
self.floor_offset = self.model.body_pos[self.model.body_name2id(
'floor')]
self.rock_mod_cache = None
self.tex_modder = TextureModder(self.sim)
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
# Set these both externally to visualize
self.visualize = False
self.viewer = None
def __init__(self, model_path, initial_qpos, n_actions, n_substeps):
if model_path.startswith('/'):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), 'assets',
model_path)
if not os.path.exists(fullpath):
raise IOError('File {} does not exist'.format(fullpath))
model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(model, nsubsteps=n_substeps)
self.modder = TextureModder(self.sim)
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
self.viewer = None
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self.seed()
self._env_setup(initial_qpos=initial_qpos)
self.initial_state = copy.deepcopy(self.sim.get_state())
self.goal = self._sample_goal()
obs = self._get_obs()
self.action_space = spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32')
self.observation_space = spaces.Dict(
dict(
desired_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
achieved_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
observation=spaces.Box(-np.inf,
np.inf,
shape=obs['observation'].shape,
dtype='float32'),
))
self.episodeAcs = []
self.episodeObs = []
self.episodeInfo = []
def _render_seg(sim: mujoco_py.MjSim, camera: str, render_height: int, render_width: int, world_xml: ET.Element):
lm = LightModder(sim)
# switch all lights off
light_names = [l.attrib['name'] for l in world_xml.findall(".//light")]
for light_name in light_names:
lm.set_active(light_name, 0)
# take screenshot
frame = sim.render(render_width * 2, render_height, camera_name=camera)
# reset lights
for light_name in light_names:
lm.set_active(light_name, 1)
return frame
def _render_rgb(sim: mujoco_py.MjSim, camera: str, render_height: int,
render_width: int, world_xml: ET.Element):
lm = LightModder(sim)
cam_names = [c.attrib['name'] for c in world_xml.findall(".//camera")]
# lights off for all other cameras except the recording one
for cam in cam_names:
light_name = _get_cam_light_name(cam)
if light_name in sim.model.light_names:
lm.set_active(light_name, 1 if cam == camera else 0)
# take screenshot
frame = sim.render(render_width * 2, render_height, camera_name=camera)
# reset camera lights
for cam in cam_names:
light_name = _get_cam_light_name(cam)
if light_name in sim.model.light_names:
lm.set_active(light_name, 1)
return frame
class ArenaModder(BaseModder):
"""
Object to handle randomization of all relevant properties of Mujoco sim
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Get start state of params to slightly jitter later
self.start_geo_size = self.model.geom_size.copy()
self.start_geom_quat = self.model.geom_quat.copy()
self.start_body_pos = self.model.body_pos.copy()
self.start_body_quat = self.model.body_quat.copy()
self.start_matid = self.model.geom_matid.copy()
self.floor_offset = self.model.body_pos[self.model.body_name2id(
'floor')]
self.rock_mod_cache = None
self.tex_modder = TextureModder(self.sim)
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
# Set these both externally to visualize
self.visualize = False
self.viewer = None
def whiten_materials(self):
self.tex_modder.whiten_materials()
def randomize(self):
self.mod_textures()
self.mod_lights()
self.mod_camera()
self.mod_walls()
self.mod_extras()
self.mod_rocks()
def mod_textures(self):
"""Randomize all the textures in the scene, including the skybox"""
for name in self.sim.model.geom_names:
if name != "billboard" and "light_disc" not in name:
self.tex_modder.rand_all(name)
##texture_sizes = [32, 64, 128, 256, 512, 1024]
##gid = self.model.geom_name2id(name)
##mid = self.model.geom_matid[gid]
##tid = self.model.mat_texid[mid]
##self.model.tex_width[tid] = sample_from_list(texture_sizes)
##self.model.tex_height[tid] = 6 * self.model.tex_width[tid]
self.tex_modder.rand_all('skybox')
def mod_lights(self):
"""Randomize pos, direction, and lights"""
# light stuff
LIGHT_RX = Range(LEFTX, RIGHTX)
LIGHT_RY = Range(BINY, DIGY)
LIGHT_RZ = Range(AFZ, AFZ + ZHIGH)
LIGHT_RANGE3D = Range3D(LIGHT_RX, LIGHT_RY, LIGHT_RZ)
LIGHT_UNIF = Range3D(Range(0, 1), Range(0, 1), Range(0, 1))
for i, name in enumerate(self.model.light_names):
lid = self.model.light_name2id(name)
# random sample 80% of any given light being on
self.light_modder.set_active(name, sample([0, 1]) < 0.8)
#self.light_modder.set_active(name, 0)
dir_xyz = sample_light_dir()
self.light_modder.set_pos(name, sample_xyz(LIGHT_RANGE3D))
self.light_modder.set_dir(name, dir_xyz)
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))
#self.model.light_directional[lid] = sample([0,1]) < 0.01
def mod_camera(self):
"""Randomize pos, direction, and fov of camera"""
# Params
XOFF = 1.0
CAM_RX = Range(ACX - XOFF, ACX + XOFF) # center of arena +/- 0.5
CAM_RY = Range(BINY + 0.2, SZ_ENDY)
CAM_RZ = Range(AFZ + ZLOW, AFZ + ZHIGH)
CAM_RANGE3D = Range3D(CAM_RX, CAM_RY, CAM_RZ)
CAM_RYAW = Range(-95, -85)
CAM_RPITCH = Range(65, 90)
CAM_RROLL = Range(85, 95) # this might actually be pitch?
CAM_ANGLE3 = Range3D(CAM_RYAW, CAM_RPITCH, CAM_RROLL)
# "The horizontal field of view is computed automatically given the
# window size and the vertical field of view." - Mujoco
# This range was calculated using: themetalmuncher.github.io/fov-calc/
# ZED has 110° hfov --> 78° vfov, Logitech C920 has 78° hfov ---> 49° vfov
# These were rounded all the way down to 40° and up to 80°. It starts
# to look a bit bad in the upper range, but I think it will help
# generalization.
CAM_RFOVY = Range(40, 80)
# Actual mods
self.cam_modder.set_pos('camera1', sample_xyz(CAM_RANGE3D))
self.cam_modder.set_quat('camera1', sample_quat(CAM_ANGLE3))
fovy = sample(CAM_RFOVY)
self.cam_modder.set_fovy('camera1', fovy)
def _set_visible(self, prefix, range_top, visible):
"""Helper function to set visibility of several objects"""
if not visible:
if range_top == 0:
name = prefix
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 0.0
for i in range(range_top):
name = "{}{}".format(prefix, i)
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 0.0
else:
if range_top == 0:
name = prefix
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 1.0
for i in range(range_top):
name = "{}{}".format(prefix, i)
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 1.0
def mod_extra_distractors(self, visible=True):
"""mod rocks and tools on the side of the arena"""
# TODO: I might consider changing these to look like rocks instead of
# just random shapes. It just looks weird to me right now. Ok for now,
# but it seems a bit off.
N = 20
self._set_visible("side_obj", N, visible)
if not visible:
return
Z_JITTER = 0.05
OBJ_XRANGE = Range(0.01, 0.1)
OBJ_YRANGE = Range(0.01, 0.1)
OBJ_ZRANGE = Range(0.01, 0.1)
OBJ_SIZE_RANGE = Range3D(OBJ_XRANGE, OBJ_YRANGE, OBJ_ZRANGE)
floor_gid = self.model.geom_name2id("floor")
left_body_id = self.model.body_name2id("left_wall")
left_geom_id = self.model.geom_name2id("left_wall")
right_body_id = self.model.body_name2id("right_wall")
right_geom_id = self.model.geom_name2id("right_wall")
left_center = self.model.body_pos[left_body_id]
left_geo = self.model.geom_size[left_geom_id]
left_height = left_center[2] + left_geo[2]
left_xrange = Range(left_center[0] - left_geo[0],
left_center[0] + left_geo[0])
left_yrange = Range(left_center[1] - left_geo[1],
left_center[1] + left_geo[1])
left_zrange = 0.02 + Range(left_height - Z_JITTER,
left_height + Z_JITTER)
left_range = Range3D(left_xrange, left_yrange, left_zrange)
right_center = self.model.body_pos[right_body_id]
right_geo = self.model.geom_size[right_geom_id]
right_height = right_center[2] + right_geo[2]
right_xrange = Range(right_center[0] - right_geo[0],
right_center[0] + right_geo[0])
right_yrange = Range(right_center[1] - right_geo[1],
right_center[1] + right_geo[1])
right_zrange = 0.02 + Range(right_height - Z_JITTER,
right_height + Z_JITTER)
right_range = Range3D(right_xrange, right_yrange, right_zrange)
for i in range(N):
name = "side_obj{}".format(i)
obj_bid = self.model.body_name2id(name)
obj_gid = self.model.geom_name2id(name)
self.model.geom_quat[obj_gid] = random_quat()
self.model.geom_size[obj_gid] = sample_xyz(OBJ_SIZE_RANGE)
self.model.geom_type[obj_gid] = sample_geom_type()
# 50% chance of invisible
if sample([0, 1]) < 0.5:
self.model.geom_rgba[obj_gid][-1] = 0.0
else:
self.model.geom_rgba[obj_gid][-1] = 1.0
## 50% chance of same color as floor and rocks
if sample([0, 1]) < 0.5:
self.model.geom_matid[obj_gid] = self.model.geom_matid[
floor_gid]
else:
self.model.geom_matid[obj_gid] = self.start_matid[obj_gid]
# 10 always on the left, 10 always on the right
if i < 10:
self.model.body_pos[obj_bid] = sample_xyz(left_range)
else:
self.model.body_pos[obj_bid] = sample_xyz(right_range)
def mod_extra_judges(self, visible=True):
"""mod NASA judges around the perimeter of the arena"""
# TODO: might want to add regions on the sides of the arena, but these
# may be covered by the distractors already
N = 5
self._set_visible("judge", N, visible)
if not visible:
return
JUDGE_XRANGE = Range(0.1, 0.2)
JUDGE_YRANGE = Range(0.1, 0.2)
JUDGE_ZRANGE = Range(0.75, 1.0)
JUDGE_SIZE_RANGE = Range3D(JUDGE_XRANGE, JUDGE_YRANGE, JUDGE_ZRANGE)
digwall_bid = self.model.body_name2id("dig_wall")
digwall_gid = self.model.geom_name2id("dig_wall")
digwall_center = self.model.body_pos[digwall_bid]
digwall_geo = self.model.geom_size[digwall_gid]
digwall_xrange = Range(-1.0 + digwall_center[0] - digwall_geo[0],
1.0 + digwall_center[0] + digwall_geo[0])
digwall_yrange = Range(digwall_center[1] + 0.5,
digwall_center[1] + 1.5)
digwall_zrange = JUDGE_ZRANGE - 0.75
digwall_range = Range3D(digwall_xrange, digwall_yrange, digwall_zrange)
for i in range(N):
name = "judge{}".format(i)
judge_bid = self.model.body_name2id(name)
judge_gid = self.model.geom_name2id(name)
#self.model.geom_quat[judge_gid] = jitter_quat(self.start_geom_quat[judge_gid], 0.05)
self.model.geom_quat[judge_gid] = random_quat()
self.model.geom_size[judge_gid] = sample_xyz(JUDGE_SIZE_RANGE)
self.model.geom_type[judge_gid] = sample_geom_type()
if self.model.geom_type[judge_gid] == 3 or self.model.geom_type[
judge_gid] == 5:
self.model.geom_size[judge_gid][1] = self.model.geom_size[
judge_gid][2]
self.model.body_pos[judge_bid] = sample_xyz(digwall_range)
# 50% chance of invisible
self.model.geom_rgba[judge_gid][-1] = sample([0, 1]) < 0.5
def mod_extra_robot_parts(self, visible=True):
"""add distractor parts of robots in the lower area of the camera frame"""
N = 3
self._set_visible("robot_part", N, visible)
if not visible:
return
# Project difference into camera coordinate frame
cam_pos = self.model.cam_pos[0]
cam_quat = np.quaternion(*self.model.cam_quat[0])
lower_range = Range3D([0.0, 0.0], [-0.2, -0.3], [-0.2, -0.3])
lower_size = Range3D([0.2, 0.6], [0.01, 0.15], [0.01, 0.15])
lower_angle = Range3D([-85.0, -95.0], [-180, 180], [-85, -95])
upper_range = Range3D([-0.6, 0.6], [-0.05, 0.05], [-0.05, 0.05])
upper_size = Range3D([0.005, 0.05], [0.005, 0.05], [0.01, 0.3])
upper_angle = Range3D([-85.0, -95.0], [-180, 180], [-85, -95])
name = "robot_part0"
lower_bid = self.model.body_name2id(name)
lower_gid = self.model.geom_name2id(name)
lower_pos = cam_pos + quaternion.rotate_vectors(
cam_quat, sample_xyz(lower_range))
self.model.body_pos[lower_bid] = lower_pos
self.model.geom_size[lower_gid] = sample_xyz(lower_size)
self.model.geom_quat[lower_gid] = sample_quat(lower_angle)
self.model.geom_type[lower_gid] = sample_geom_type(reject=["capsule"])
if self.model.geom_type[lower_gid] == 5:
self.model.geom_size[lower_gid][0] = self.model.geom_size[
lower_gid][2]
for i in range(1, 10):
name = "robot_part{}".format(i)
upper_bid = self.model.body_name2id(name)
upper_gid = self.model.geom_name2id(name)
upper_pos = lower_pos + sample_xyz(upper_range)
self.model.body_pos[upper_bid] = upper_pos
self.model.geom_size[upper_gid] = sample_xyz(upper_size)
self.model.geom_type[upper_gid] = sample_geom_type()
# 50% of the time, choose random angle instead reasonable angle
if sample([0, 1]) < 0.5:
self.model.geom_quat[upper_gid] = sample_quat(upper_angle)
else:
self.model.geom_quat[upper_gid] = random_quat()
def mod_extra_arena_structure(self, visible=True):
"""add randomized structure of the arena in the background with
pillars and a crossbar"""
N = 16
self._set_visible("arena_structure", N, visible)
if not visible:
return
STARTY = DIGY + 3.0
ENDY = DIGY + 5.0
XBAR_SIZE = Range3D([10.0, 20.0], [0.05, 0.1], [0.2, 0.5])
XBAR_RANGE = Range3D([0.0, 0.0], [STARTY, ENDY], [3.0, 5.0])
STRUCTURE_SIZE = Range3D([0.05, 0.1], [0.05, 0.1], [3.0, 6.0])
STRUCTURE_RANGE = Range3D([np.nan, np.nan], [STARTY, ENDY], [0.0, 0.0])
x_range = np.linspace(ACX - 10.0, ACX + 10.0, 15)
# crossbar
name = "arena_structure{}".format(N - 1)
xbar_bid = self.model.body_name2id(name)
xbar_gid = self.model.geom_name2id(name)
self.model.geom_quat[xbar_gid] = jitter_quat(
self.start_geom_quat[xbar_gid], 0.01)
self.model.geom_size[xbar_gid] = sample_xyz(XBAR_SIZE)
self.model.body_pos[xbar_bid] = sample_xyz(XBAR_RANGE)
### 10% chance of invisible
##if sample([0,1]) < 0.1:
## self.model.geom_rgba[xbar_gid][-1] = 0.0
##else:
## self.model.geom_rgba[xbar_gid][-1] = 1.0
for i in range(N - 1):
name = "arena_structure{}".format(i)
arena_structure_bid = self.model.body_name2id(name)
arena_structure_gid = self.model.geom_name2id(name)
STRUCTURE_RANGE[0] = Range(x_range[i] - 0.1, x_range[i] + 0.1)
self.model.geom_quat[arena_structure_gid] = jitter_quat(
self.start_geom_quat[arena_structure_gid], 0.01)
self.model.geom_size[arena_structure_gid] = sample_xyz(
STRUCTURE_SIZE)
self.model.body_pos[arena_structure_bid] = sample_xyz(
STRUCTURE_RANGE)
self.model.geom_matid[arena_structure_gid] = self.model.geom_matid[
xbar_gid]
# 10% chance of invisible
if sample([0, 1]) < 0.1:
self.model.geom_rgba[arena_structure_gid][-1] = 0.0
else:
self.model.geom_rgba[arena_structure_gid][-1] = 1.0
def mod_extra_arena_background(self, visible=True, N=10):
""" """
self._set_visible("background", N, visible)
if not visible:
return
BACKGROUND_XRANGE = Range(0.1, 0.5)
BACKGROUND_YRANGE = Range(0.1, 0.5)
BACKGROUND_ZRANGE = Range(0.1, 1.0)
BACKGROUND_SIZE_RANGE = Range3D(BACKGROUND_XRANGE, BACKGROUND_YRANGE,
BACKGROUND_ZRANGE)
digwall_bid = self.model.body_name2id("dig_wall")
digwall_gid = self.model.geom_name2id("dig_wall")
c = digwall_center = self.model.body_pos[digwall_bid]
background_range = Range3D([c[0] - 4.0, c[0] + 4.0],
[c[1] + 8.0, c[1] + 12.0], [0.0, 5.0])
for i in range(N):
name = "background{}".format(i)
background_bid = self.model.body_name2id(name)
background_gid = self.model.geom_name2id(name)
self.model.geom_quat[background_gid] = random_quat()
self.model.geom_size[background_gid] = sample_xyz(
BACKGROUND_SIZE_RANGE)
self.model.geom_type[background_gid] = sample_geom_type()
self.model.body_pos[background_bid] = sample_xyz(background_range)
def mod_extra_lights(self, visible=True):
""""""
#visible = False
MODE_TARGETBODY = 3
STARTY = DIGY + 3.0
ENDY = DIGY + 5.0
LIGHT_RANGE = Range3D([ACX - 2.0, ACX + 2.0], [STARTY, ENDY],
[3.0, 5.0])
any_washed = False
for i in range(3):
name = "extra_light{}".format(i)
lid = self.model.light_name2id(name)
self.model.light_active[lid] = visible
if not visible:
return
self.model.light_pos[lid] = sample_xyz(LIGHT_RANGE)
self.model.light_dir[lid] = sample_light_dir()
self.model.light_directional[lid] = 0
### 3% chance of directional light, washing out colors
### (P(at least 1 will be triggered) ~= 0.1) = 1 - 3root(p)
washout = sample([0, 1]) < 0.03
any_washed = any_washed or washout
self.model.light_directional[lid] = washout
if any_washed:
self.mod_extra_light_discs(
visible=visible and sample([0, 1]) < 0.9)
else:
self.mod_extra_light_discs(visible=False)
def mod_extra_light_discs(self, visible=True):
""""""
N = 10
self._set_visible("light_disc", N, visible)
if not visible:
return
Z_JITTER = 0.05
DISC_XRANGE = Range(0.1, 4.0)
DISC_YRANGE = Range(0.1, 4.0)
DISC_ZRANGE = Range(0.1, 4.0)
DISC_SIZE_RANGE = Range3D(DISC_XRANGE, DISC_YRANGE, DISC_ZRANGE)
OUTR = 20.0
INR = 10.0
floor_bid = self.model.body_name2id("floor")
c = self.model.body_pos[floor_bid]
disc_xrange = Range(c[0] - OUTR, c[0] + OUTR)
disc_yrange = Range(c[1], c[1] + OUTR)
disc_zrange = Range(-5.0, 10.0)
disc_range = Range3D(disc_xrange, disc_yrange, disc_zrange)
for i in range(N):
name = "light_disc{}".format(i)
disc_bid = self.model.body_name2id(name)
disc_gid = self.model.geom_name2id(name)
disc_mid = self.model.geom_matid[disc_gid]
self.model.geom_quat[disc_gid] = random_quat()
self.model.geom_size[disc_gid] = sample_xyz(DISC_SIZE_RANGE)
self.model.geom_type[disc_gid] = sample_geom_type()
# keep trying to place the disc until it lands outside of blocking stuff
# (they will land in a u shape, because they are sampled with a
# constrain to not land in the middle)
while True:
xyz = sample_xyz(disc_range)
if ((xyz[0] > (c[0] - INR) and xyz[0] < (c[0] + INR))
and (xyz[1] > (c[1] - INR) and xyz[1] < (c[1] + INR))):
continue
else:
self.model.geom_pos[disc_gid] = xyz
break
# 50% chance of invisible
if sample([0, 1]) < 0.5:
self.model.geom_rgba[disc_gid][-1] = 0.0
else:
self.model.geom_rgba[disc_gid][-1] = 1.0
def mod_extras(self, visible=True):
"""
Randomize extra properties of the world such as the extra rocks on the side
of the arena wal
The motivation for these mods are that it seems likely that these distractor
objects, judges, etc, in the real images could degrade the performance
of the detector.
Artifacts:
- Rocks and tools on edges of bin
- NASA judges around the perimeter
- Parts of the robot in the frame
- Background arena structure and crowd
- Bright light around edges of arena
"""
self.mod_extra_distractors(visible)
self.mod_extra_robot_parts(visible)
self.mod_extra_lights(visible)
# 10% of the time, hide the other distractor pieces
self.mod_extra_judges(visible=visible and sample([0, 1]) < 0.9)
self.mod_extra_arena_structure(
visible=visible and sample([0, 1]) < 0.9)
self.mod_extra_arena_background(
visible=visible and sample([0, 1]) < 0.9)
def mod_walls(self):
"""
Randomize the x, y, and orientation of the walls slights.
Also drastically randomize the height of the walls. In many cases they won't
be seen at all. This will allow the model to generalize to scenarios without
walls, or where the walls and geometry is slightly different than the sim
model
"""
wall_names = ["left_wall", "right_wall", "bin_wall", "dig_wall"]
for name in wall_names:
geom_id = self.model.geom_name2id(name)
body_id = self.model.body_name2id(name)
jitter_x = Range(-0.2, 0.2)
jitter_y = Range(-0.2, 0.2)
jitter_z = Range(-0.75, 0.0)
jitter3D = Range3D(jitter_x, jitter_y, jitter_z)
self.model.body_pos[
body_id] = self.start_body_pos[body_id] + sample_xyz(jitter3D)
self.model.body_quat[body_id] = jitter_quat(
self.start_body_quat[body_id], 0.005)
if sample([0, 1]) < 0.05:
self.model.body_pos[body_id][2] = -2.0
def mod_dirt(self):
"""Randomize position and rotation of dirt"""
# TODO: 50% chance to flip the dirt pile upsidedown, then we will have
# to account for this in calculations
# dirt stuff
DIRT_RX = Range(0.0, 0.3)
DIRT_RY = Range(0.0, 0.3)
DIRT_RZ = Range(-0.05, 0.03)
DIRT_RANGE3D = Range3D(DIRT_RX, DIRT_RY, DIRT_RZ)
DIRT_RYAW = Range(-180, 180)
DIRT_RPITCH = Range(-90.5, -89.5)
DIRT_RROLL = Range(-0.5, 0.5)
DIRT_ANGLE3 = Range3D(DIRT_RYAW, DIRT_RPITCH, DIRT_RROLL)
dirt_bid = self.model.body_name2id("dirt")
dirt_gid = self.model.geom_name2id("dirt")
dirt_mid = self.model.geom_dataid[dirt_gid]
# randomize position and yaw of dirt
self.model.body_pos[dirt_bid] = self.start_body_pos[
dirt_bid] + sample_xyz(DIRT_RANGE3D)
self.model.geom_quat[dirt_gid] = sample_quat(DIRT_ANGLE3)
vert_adr = self.model.mesh_vertadr[dirt_mid]
vert_num = self.model.mesh_vertnum[dirt_mid]
mesh_verts = self.model.mesh_vert[vert_adr:vert_adr + vert_num]
rot_quat = self.model.geom_quat[dirt_gid]
rots = quaternion.rotate_vectors(
np.quaternion(*rot_quat).normalized(), mesh_verts)
mesh_abs_pos = self.floor_offset + self.model.body_pos[dirt_bid] + rots
#xy_indexes = mesh_abs_pos[:, 0:2]
#z_heights = mesh_abs_pos[:, 2]
# Return a function that the user can call to get the approximate
# height of an xy location
def local_mean_height(xy):
"""
Take an xy coordinate, and the approximate z height of the mesh at that
location. It works decently.
Uses a weighted average mean of all points within a threshold of xy.
"""
# grab all mesh points within threshold euclidean distance of xy
gt0_xyz = mesh_abs_pos[mesh_abs_pos[:, 2] > 0.01]
eudists = np.sum(np.square(gt0_xyz[:, 0:2] - xy), axis=1)
indices = eudists < 0.1
close_xyz = gt0_xyz[indices]
# if there are any nearby points above 0
if np.count_nonzero(close_xyz[:, 2]) > 0:
# weights for weighted sum. closer points to xy have higher weight
weights = 1 / (eudists[indices])
weights = np.expand_dims(weights / np.sum(weights), axis=1)
pos = np.sum(close_xyz * weights, axis=0)
# show an "o" and a marker where the height is
if self.visualize:
self.viewer.add_marker(pos=pos,
label="o",
size=np.array([0.01, 0.01, 0.01]),
rgba=np.array([0.0, 1.0, 0.0, 1.0]))
# approximate z height of ground
return pos[2]
else:
return 0
def always_zero(xy):
return 0
dirt_height_xy = local_mean_height
#dirt_height_xy = always_zero
return dirt_height_xy
def mod_rocks(self):
"""
Randomize the rocks so that the model will generalize to competition rocks
This randomizations currently being done are:
- Positions (within guesses of competition regions)
- Orientations
- Shuffling the 3 rock meshes so that they can be on the left, middle, or right
- Generating new random rock meshes every n runs (with Blender)
"""
# Rock placement range parameters
ROCK_LANEX = 0.4 # width parameters of x range
OUTER_EXTRA = 0.5 # how much farther rocks should go out on the right and left lanes
ROCK_BUFFX = 0.2 # distacne between rock lanes
# How far into the obstacle zone the rocks should start.
ROCK_START_OFFSET = 0.2
MID_START_OFFSET = 0.4 # bit more for middle rock
ROCK_RY = Range(OBS_SY + ROCK_START_OFFSET, OBS_ENDY)
MID_RY = Range(OBS_SY + MID_START_OFFSET, OBS_ENDY)
ROCK_RZ = Range(AFZ - 0.02, AFZ + 0.2)
# Position dependent ranges
LEFT_RX = Range(-3 * ROCK_LANEX - OUTER_EXTRA,
-ROCK_LANEX - ROCK_BUFFX)
MID_RX = Range(-ROCK_LANEX, ROCK_LANEX)
RIGHT_RX = Range(ROCK_BUFFX + ROCK_LANEX, 3 * ROCK_LANEX + OUTER_EXTRA)
# Form full 3D sample range
LEFT_ROCK_RANGE = Range3D(LEFT_RX, ROCK_RY, ROCK_RZ)
MID_ROCK_RANGE = Range3D(MID_RX, MID_RY, ROCK_RZ)
RIGHT_ROCK_RANGE = Range3D(RIGHT_RX, ROCK_RY, ROCK_RZ)
ROCK_RANGES = [LEFT_ROCK_RANGE, MID_ROCK_RANGE, RIGHT_ROCK_RANGE]
# actual mods
rock_body_ids = {}
rock_geom_ids = {}
rock_mesh_ids = {}
max_height_idxs = {}
rot_cache = {}
#max_height_xys = {}
dirt_height_xy = self.mod_dirt()
for name in self.model.geom_names:
if name[:4] != "rock":
continue
geom_id = self.model.geom_name2id(name)
body_id = self.model.body_name2id(name)
mesh_id = self.model.geom_dataid[geom_id]
rock_geom_ids[name] = geom_id
rock_body_ids[name] = body_id
rock_mesh_ids[name] = mesh_id
# Rotate the rock and get the z value of the highest point in the
# rotated rock mesh
rot_quat = random_quat()
vert_adr = self.model.mesh_vertadr[mesh_id]
vert_num = self.model.mesh_vertnum[mesh_id]
mesh_verts = self.model.mesh_vert[vert_adr:vert_adr + vert_num]
rots = quaternion.rotate_vectors(
np.quaternion(*rot_quat).normalized(), mesh_verts)
self.model.geom_quat[geom_id] = rot_quat
max_height_idx = np.argmax(rots[:, 2])
max_height_idxs[name] = max_height_idx
rot_cache[name] = rots
# Shuffle the positions of the rocks (l or m or r)
shuffle_names = list(rock_body_ids.keys())
random.shuffle(shuffle_names)
self.rock_mod_cache = []
for i in range(len(shuffle_names)):
name = shuffle_names[i]
rots = rot_cache[name]
self.model.body_pos[rock_body_ids[name]] = np.array(
sample_xyz(ROCK_RANGES[i]))
max_height_idx = max_height_idxs[name]
xyz_for_max_z = rots[max_height_idx]
# xyz coords in global frame
global_xyz = self.floor_offset + xyz_for_max_z + self.model.body_pos[
rock_body_ids[name]]
gxy = global_xyz[0:2]
max_height = global_xyz[2]
if self.visualize:
self.viewer.add_marker(pos=global_xyz,
label="m",
size=np.array([0.01, 0.01, 0.01]),
rgba=np.array([0.0, 0.0, 1.0, 1.0]))
dirt_z = dirt_height_xy(gxy)
#dirt_z = 0
#print(name, dirt_z)
z_height = max_height - dirt_z
self.rock_mod_cache.append((name, z_height))
def get_ground_truth(self):
"""
self.rock_mod_cache set from self.mod_rocks
Return 1d numpy array of 9 elements for positions of all 3 rocks including:
- rock x dist from cam
- rock y dist from cam
- rock z height from arena floor
"""
cam_pos = self.model.cam_pos[0]
#line_pos = self.floor_offset + np.array([0.0, 0.75, 0.0])
#self.viewer.add_marker(pos=line_pos)
##r0_pos = self.floor_offset + self.model.body_pos[self.model.body_name2id('rock0')]
##r1_pos = self.floor_offset + self.model.body_pos[self.model.body_name2id('rock1')]
##r2_pos = self.floor_offset + self.model.body_pos[self.model.body_name2id('rock2')]
##r0_diff = r0_pos - cam_pos
##r1_diff = r1_pos - cam_pos
##r2_diff = r2_pos - cam_pos
ground_truth = np.zeros(9, dtype=np.float32)
for i, slot in enumerate(self.rock_mod_cache):
name = slot[0]
z_height = slot[1]
pos = self.floor_offset + self.model.body_pos[
self.model.body_name2id(name)]
diff = pos - cam_pos
# Project difference into camera coordinate frame
cam_angle = quaternion.as_euler_angles(
np.quaternion(*self.model.cam_quat[0]))[0]
cam_angle += np.pi / 2
in_cam_frame = np.zeros_like(diff)
x = diff[1]
y = -diff[0]
in_cam_frame[0] = x * np.cos(cam_angle) + y * np.sin(cam_angle)
in_cam_frame[1] = -x * np.sin(cam_angle) + y * np.cos(cam_angle)
in_cam_frame[2] = z_height
# simple check that change of frame is mathematically valid
assert (np.isclose(np.sum(np.square(diff[:2])),
np.sum(np.square(in_cam_frame[:2]))))
# swap positions to match ROS standard coordinates
ground_truth[3 * i + 0] = in_cam_frame[0]
ground_truth[3 * i + 1] = in_cam_frame[1]
ground_truth[3 * i + 2] = in_cam_frame[2]
text = "{0} x: {1:.2f} y: {2:.2f} height:{3:.2f}".format(
name, ground_truth[3 * i + 0], ground_truth[3 * i + 1],
z_height)
##text = "x: {0:.2f} y: {1:.2f} height:{2:.2f}".format(ground_truth[3*i+0], ground_truth[3*i+1], z_height)
##text = "height:{0:.2f}".format(z_height)
if self.visualize:
self.viewer.add_marker(pos=pos, label=text, rgba=np.zeros(4))
return ground_truth
class SimManager(object):
"""Object to generate sequence of images with their transforms"""
def __init__(self,
filepath,
random_params={},
gpu_render=False,
gui=False,
display_data=False):
self.model = load_model_from_path(filepath)
self.sim = MjSim(self.model)
self.filepath = filepath
self.gui = gui
self.display_data = display_data
# Take the default random params and update anything we need
self.RANDOM_PARAMS = {}
self.RANDOM_PARAMS.update(random_params)
if gpu_render:
self.viewer = MjViewer(self.sim)
else:
self.viewer = None
# Get start state of params to slightly jitter later
self.START_GEOM_POS = self.model.geom_pos.copy()
self.START_GEOM_SIZE = self.model.geom_size.copy()
self.START_GEOM_QUAT = self.model.geom_quat.copy()
self.START_BODY_POS = self.model.body_pos.copy()
self.START_BODY_QUAT = self.model.body_quat.copy()
self.START_MATID = self.model.geom_matid.copy()
#self.FLOOR_OFFSET = self.model.body_pos[self.model.body_name2id('floor')]
self.tex_modder = TextureModder(self.sim)
self.tex_modder.whiten_materials(
) # ensures materials won't impact colors
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
self.start_obj_pose = self.sim.data.get_joint_qpos(
'object:joint').copy()
def get_data(self, num_images=10):
"""
Returns camera intrinsics, and a sequence of images, pose transforms, and
camera transforms
"""
# randomize the scene
self._rand_textures()
self._rand_lights()
self._rand_object()
self._rand_walls()
self._rand_distract()
sequence = defaultdict(list)
context = {}
# object pose
obj_pose, robot_pose = self._get_ground_truth()
context["obj_world_pose"] = obj_pose
context["robot_world_pose"] = robot_pose
self._cam_step = 0
self._cam_choices = np.array([[-1.75, 0, 2], [-1.75, 0, 1.62],
[-1.3, 1.7, 1.62]])
self._curr_cam_pos = self._cam_choices[0]
for i in range(num_images):
self._next_camera()
self._forward()
img = self._get_cam_frame()
sequence["img"].append(img)
cam_pos = self.cam_modder.get_pos("camera1")
cam_quat = self.cam_modder.get_quat("camera1")
cam_pose = np.concatenate([cam_pos, cam_quat]).astype(np.float32)
sequence["cam_pose"].append(cam_pose)
cam_id = self.cam_modder.get_camid("camera1")
fovy = self.sim.model.cam_fovy[cam_id]
width, height = 640, 480
f = 0.5 * height / math.tan(fovy * math.pi / 360)
camera_intrinsics = np.array(
((f, 0, width / 2), (0, f, height / 2), (0, 0, 1)))
context['cam_matrix'] = camera_intrinsics
return context, sequence
def _forward(self):
"""Advances simulator a step (NECESSARY TO MAKE CAMERA AND LIGHT MODDING WORK)
And add some visualization"""
self.sim.forward()
if self.viewer and self.gui:
# Get angle of camera and display it
quat = np.quaternion(*self.model.cam_quat[0])
ypr = quaternion.as_euler_angles(quat) * 180 / np.pi
cam_pos = self.model.cam_pos[0]
cam_fovy = self.model.cam_fovy[0]
#self.viewer.add_marker(pos=cam_pos, label="CAM: {}{}".format(cam_pos, ypr))
#self.viewer.add_marker(pos=cam_pos, label="CAM: {}".format(ypr))
#self.viewer.add_marker(pos=cam_pos, label="CAM: {}".format(cam_pos))
self.viewer.add_marker(pos=cam_pos,
label="FOVY: {}, CAM: {}".format(
cam_fovy, cam_pos))
self.viewer.render()
def _get_ground_truth(self):
"""
Return the position to the robot, and quaternion to the robot quaternion
7 dim total
"""
robot_gid = self.sim.model.geom_name2id('base_link')
obj_gid = self.sim.model.geom_name2id('object')
# only x and y pos needed
obj_world_pos = self.sim.data.geom_xpos[obj_gid]
robot_world_pos = self.sim.data.geom_xpos[robot_gid]
# obj_pos_in_robot_frame = (self.sim.data.geom_xpos[obj_gid] - self.sim.data.geom_xpos[robot_gid])[:2]
# robot_quat = quaternion.as_quat_array(self.model.geom_quat[robot_gid].copy())
obj_world_quat = self.model.geom_quat[obj_gid].copy()
robot_world_quat = self.model.geom_quat[robot_gid].copy()
# # want quat of obj relative to robot frame
# # obj_q = robot_q * localrot
# # robot_q.inv * obj_q = localrot
# rel_quat = quaternion.as_float_array(robot_quat.inverse() * obj_quat)
# pose = np.concatenate([obj_pos_in_robot_frame, rel_quat]).astype(np.float32)
obj_pose = self.sim.data.get_joint_qpos('object:joint').copy()
robot_pose = np.concatenate([robot_world_pos,
robot_world_quat]).astype(np.float32)
return obj_pose, robot_pose
def _get_cam_frame(self, ground_truth=None):
"""Grab an image from the camera (224, 244, 3) to feed into CNN"""
#IMAGE_NOISE_RVARIANCE = Range(0.0, 0.0001)
cam_img = self.sim.render(
640, 480, camera_name='camera1'
)[::-1, :, :] # Rendered images are upside-down.
# make camera crop be more like kinect
#cam_img = self.sim.render(854, 480, camera_name='camera1')[::-1, 107:-107, :] # Rendered images are upside-down.
#image_noise_variance = sample(IMAGE_NOISE_RVARIANCE)
#cam_img = (skimage.util.random_noise(cam_img, mode='gaussian', var=image_noise_variance) * 255).astype(np.uint8)
if self.display_data:
print(ground_truth)
#label = str(ground_truth[3:6])
display_image(cam_img, mode='preproc') #, label)
# cam_img = preproc_image(cam_img)
return cam_img
def _randomize(self):
self._rand_textures()
self._rand_camera()
self._rand_lights()
#self._rand_robot()
self._rand_object()
self._rand_walls()
self._rand_distract()
def _rand_textures(self):
"""Randomize all the textures in the scene, including the skybox"""
bright = np.random.binomial(1, 0.5)
for name in self.sim.model.geom_names + ('skybox', ):
self.tex_modder.rand_all(name)
if bright:
self.tex_modder.brighten(name, np.random.randint(0, 150))
def _rand_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
cam_choices = np.array([[-1.75, 0, 1.62], [-1.3, 1.7, 1.62],
[-1.75, 0, 2]])
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 _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_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_robot(self):
"""Randomize joint angles and jitter orientation"""
jnt_shape = self.sim.data.qpos.shape
self.sim.data.qpos[:] = sample_joints(self.model.jnt_range, jnt_shape)
robot_gid = self.model.geom_name2id('robot_table_link')
self.model.geom_quat[robot_gid] = jitter_quat(
self.START_GEOM_QUAT[robot_gid], 0.01)
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)
#T_X = Range(-0.1, 0.1)
#T_Y = Range(-0.1, 0.1)
#T_Z = Range(-0.1, 0.1)
#T_R3D = Range3D(T_X, T_Y, T_Z)
#self.model.body_pos[table_bid] = self.START_BODY_POS[table_bid] + sample_xyz(T_R3D)
## randomize orientation a wee bit
#self.model.geom_quat[table_gid] = jitter_quat(self.START_GEOM_QUAT[table_gid], 0.01)
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 _set_visible(self, prefix, range_top, visible):
"""Helper function to set visibility of several objects"""
if not visible:
if range_top == 0:
name = prefix
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 0.0
for i in range(range_top):
name = "{}{}".format(prefix, i)
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 0.0
else:
if range_top == 0:
name = prefix
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 1.0
for i in range(range_top):
name = "{}{}".format(prefix, i)
gid = self.model.geom_name2id(name)
self.model.geom_rgba[gid][-1] = 1.0
def __init__(self, FLAGS):
super().__init__(FLAGS)
if self.FLAGS['baxter']:
self.robot_offset = lambda: self.model.body_pos[self.name2bid(
'base_ground')]
else:
self.robot_offset = lambda: self.model.body_pos[self.name2bid(
'robot_table_floor')]
self.table_center = lambda: TABLES[self.FLAGS['default_table']][
'pos'] + self.robot_offset(
) + [0, 0, TABLES[self.FLAGS['default_table']]['wood'][2]]
self.LIGHT = {
'ambient': self.model.light_ambient,
'active': self.model.light_active,
'castshadow': self.model.light_castshadow,
'diffuse': self.model.light_diffuse,
'dir': self.model.light_dir,
'pos': self.model.light_pos,
'specular': self.model.light_specular
}
self.START_BODY_POS = self.model.body_pos.copy()
self.START_LIGHT = {}
for key in self.LIGHT:
self.START_LIGHT[key] = self.LIGHT[key].copy()
self.tex_modder = TextureModder(self.sim)
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
if self.FLAGS['domrand']:
self.tex_modder.whiten_materials()
self.goal = {}
# mean camera position defined relative to table center
if FLAGS['baxter']:
self.CAMERA_POS_MEAN = np.array([-0.64, 0.025, 1.0])
else:
self.CAMERA_POS_MEAN = np.array([-0.65, 0.0, 0.68])
# establish values for discretizing
self.X = self.FLAGS['ACTS']['x']
self.Y = self.FLAGS['ACTS']['y']
self.YAW = self.FLAGS['ACTS']['yaw']
self.DIST = self.FLAGS['ACTS']['dist']
self.action_space = self.FLAGS['action_space']
self.observation_space = self.FLAGS['observation_space']
if self.FLAGS['goal_conditioned']:
self.goal['array'] = np.zeros(self.FLAGS['state_shape'])
if self.FLAGS['use_image']:
self.goal['image'] = np.zeros(self.FLAGS['image_shape'])
self.paddle_bid = self.name2bid('paddle')
self.paddle_gid = self.name2gid('paddle')
self.N = int(
0.5 /
self.dt) # N should correspond to about 0.5 seconds of timestep
self.consecutive_fences = 0
self.escape_count = 0
self._set_invisiwall()
self._reset_sim()
class ObjectCollectionEnv(BaseEnv):
"""Custom muluti-object environment"""
def __init__(self, FLAGS):
super().__init__(FLAGS)
if self.FLAGS['baxter']:
self.robot_offset = lambda: self.model.body_pos[self.name2bid(
'base_ground')]
else:
self.robot_offset = lambda: self.model.body_pos[self.name2bid(
'robot_table_floor')]
self.table_center = lambda: TABLES[self.FLAGS['default_table']][
'pos'] + self.robot_offset(
) + [0, 0, TABLES[self.FLAGS['default_table']]['wood'][2]]
self.LIGHT = {
'ambient': self.model.light_ambient,
'active': self.model.light_active,
'castshadow': self.model.light_castshadow,
'diffuse': self.model.light_diffuse,
'dir': self.model.light_dir,
'pos': self.model.light_pos,
'specular': self.model.light_specular
}
self.START_BODY_POS = self.model.body_pos.copy()
self.START_LIGHT = {}
for key in self.LIGHT:
self.START_LIGHT[key] = self.LIGHT[key].copy()
self.tex_modder = TextureModder(self.sim)
self.cam_modder = CameraModder(self.sim)
self.light_modder = LightModder(self.sim)
if self.FLAGS['domrand']:
self.tex_modder.whiten_materials()
self.goal = {}
# mean camera position defined relative to table center
if FLAGS['baxter']:
self.CAMERA_POS_MEAN = np.array([-0.64, 0.025, 1.0])
else:
self.CAMERA_POS_MEAN = np.array([-0.65, 0.0, 0.68])
# establish values for discretizing
self.X = self.FLAGS['ACTS']['x']
self.Y = self.FLAGS['ACTS']['y']
self.YAW = self.FLAGS['ACTS']['yaw']
self.DIST = self.FLAGS['ACTS']['dist']
self.action_space = self.FLAGS['action_space']
self.observation_space = self.FLAGS['observation_space']
if self.FLAGS['goal_conditioned']:
self.goal['array'] = np.zeros(self.FLAGS['state_shape'])
if self.FLAGS['use_image']:
self.goal['image'] = np.zeros(self.FLAGS['image_shape'])
self.paddle_bid = self.name2bid('paddle')
self.paddle_gid = self.name2gid('paddle')
self.N = int(
0.5 /
self.dt) # N should correspond to about 0.5 seconds of timestep
self.consecutive_fences = 0
self.escape_count = 0
self._set_invisiwall()
self._reset_sim()
#self.viewer.add_marker(pos=cam_pos, label="CAM: {}".format(cam_pos))
def _get_object_poses(self):
objs = []
for i in range(self.FLAGS['num_objects']):
obj = 'object{}:joint'.format(i)
pose = self.sim.data.get_joint_qpos(obj).copy()
pose[:3] -= self.table_center()
if not self.FLAGS['use_quat']:
pose = pose[:3]
objs.append(pose)
objs = np.stack(objs)
return objs
def _get_obs(self):
self.sim.step()
array = self._get_object_poses()
obs = {'array': array, 'single': np.zeros(4)}
if self.FLAGS['use_image']:
image = self._get_cam_frame().astype(np.float32)
image = cv2.resize(image,
self.FLAGS['image_shape'][:2],
interpolation=cv2.INTER_AREA)
image /= 255.0
obs.update({'image': image})
if self.FLAGS['goal_conditioned']:
obs.update({'goal_array': self.goal['array']})
if self.FLAGS['use_image']:
obs.update({'goal_image': self.goal['image']})
return obs
def _geofence_escape(self):
"""check if any of the cubes have escaped the table. this should not be possible
but still happens due to some glitch with mujoco
return True if any cube escapes
"""
cubes = self._get_object_poses()
cube_size = self.sim.model.geom_size[self.name2gid('object0')]
table_range = 0.5 * TABLES[self.FLAGS['default_table']]['wood']
table_range[
2] = 0.1 # be forgiving on the height, since we already have object_fell to check for drops
table_box = (1 + 0.01) * np.stack([-table_range, table_range], axis=-1)
for c in cubes:
cube_box = c[:3, None] + np.stack([-cube_size, cube_size], axis=-1)
if not overlap3D(cube_box, table_box):
return True
return False
def _object_fell(self):
"""check if any of the cubes have fallen off the table"""
cubes = self._get_object_poses()
for c in cubes:
if c[2] < -0.01:
return True
return False
def step(self, action):
# convert from tuple to dictionary. this is pretty risky and bad practice, but should throw errors for shape mismatch in most cases *crosses fingers*
if isinstance(action, tuple) or isinstance(action, np.ndarray):
names = ['x', 'y', 'yaw']
names += ['dist'] if self.FLAGS['use_dist'] else []
sorted_names = list(sorted(names))
action = {
sorted_names[i]: action[i]
for i in range(len(sorted_names))
}
action = copy.deepcopy(action)
safe_action = self._set_action(action)
reward = 0.0
if self._geofence_escape() or self._object_fell():
reward += -10.0
done = True
print('AHHH')
else:
done = False
obs = self._get_obs()
if self.FLAGS['penalize_invisiwall'] and safe_action != '':
if 'wall' in safe_action:
reward += -1.0
info = {'seed': self.current_seed}
out_obs = self.get_out_obs()
return out_obs, reward, done, info
def _set_action(self, action):
"""Apply the action to the env
Return True is safe action, else false
"""
self._randomize()
contact_invisiwall = False
if self.FLAGS['discrete']:
x = self.X[action['x']]
y = self.Y[action['y']]
yaw = self.YAW[action['yaw']]
if self.FLAGS['use_dist']:
dist = self.DIST[1 + action['dist']]
else:
dist = self.FLAGS['max_dx']
else:
spaces = self.action_space.spaces
def clip_act(key):
"""clip and then map back to semantic action scale"""
clipped = np.clip(action[key], -1.0, 1.0)
return unmap_continuous(key, clipped, self.FLAGS)
x = clip_act('x')
y = clip_act('y')
yaw = clip_act('yaw')
if self.FLAGS['use_dist']:
dist = clip_act('dist')
else:
dist = self.FLAGS['max_dx']
# Parse action
s_xyz = np.array([x, y, 0.5])
s_xyz += self.table_center()
self.model.geom_pos[self.paddle_gid] = s_xyz
self.sim.data.geom_xpos[self.paddle_gid] = s_xyz
quat = quaternion.from_euler_angles(0, 0, yaw).normalized().components
self.sim.model.geom_quat[self.paddle_gid] = quat
xp_aid = self.model.actuator_name2id('xp')
xv_aid = self.model.actuator_name2id('xv')
yp_aid = self.model.actuator_name2id('yp')
yv_aid = self.model.actuator_name2id('yv')
zp_aid = self.model.actuator_name2id('zp')
zv_aid = self.model.actuator_name2id('zv')
self.sim.data.set_joint_qpos('paddle:slidex', np.array([0]))
self.sim.data.set_joint_qpos('paddle:slidey', np.array([0]))
self.sim.data.set_joint_qpos('paddle:slidez', np.array([0]))
self.sim.data.set_joint_qpos('paddle:hingez', np.array([0]))
self.sim.data.ctrl[:] = 0.0
# Set goal positions of paddle (lower to targetz, then push to targetx and targety based on action)
if self.FLAGS['baxter']:
targetz = -0.925 + 0.83
else:
targetz = 0.69
targetx, targety = s_xyz[0] + np.cos(yaw) * dist, s_xyz[1] + np.sin(
yaw) * dist
# reverse push at the end so that we are not in contact with block when we lift.
btargetx, btargety = s_xyz[0] + np.cos(yaw) * (
0.5 * dist), s_xyz[1] + np.sin(yaw) * (0.5 * dist)
# Draw indicator of paddle target pos
target = np.array([targetx, targety, targetz])
site_id = self.name2sid('target0')
self.sim.model.site_rgba[site_id] = np.zeros(4)
if self.FLAGS['render']:
self.sim.model.site_pos[site_id] = target
self.sim.model.site_quat[site_id] = quat
# Vertical PID controller to lower and raise
P = 400.0
D = 40.0
v_pid = PID(
P,
D,
curr_fn=lambda: self.sim.data.geom_xpos[self.paddle_gid][2],
goal=targetz)
# Horizontal (xy) PID controllers to push
HP = 600.0
HD = 60.0
hx_pid = PID(
HP,
HD,
curr_fn=lambda: self.sim.data.geom_xpos[self.paddle_gid][0],
goal=targetx)
hy_pid = PID(
HP,
HD,
curr_fn=lambda: self.sim.data.geom_xpos[self.paddle_gid][1],
goal=targety)
# Lower the paddle
for i in range(self.N):
self.sim.data.ctrl[zp_aid], self.sim.data.ctrl[
zv_aid] = v_pid.step()
self.sim.step()
contact_invisiwall = contact_invisiwall or self._collide_wall()
if self.FLAGS['render']:
self.viewer.render()
# Push forward to the target position
for i in range(int(1.5 * self.N)):
#self.sim.data.ctrl[zp_aid], self.sim.data.ctrl[zv_aid] = v_pid.step()
self.sim.data.ctrl[xp_aid], self.sim.data.ctrl[
xv_aid] = hx_pid.step()
self.sim.data.ctrl[yp_aid], self.sim.data.ctrl[
yv_aid] = hy_pid.step()
contact_invisiwall = contact_invisiwall or self._collide_wall()
self.sim.step()
if self.FLAGS['render']:
self.viewer.render()
# Reverse a bit
hx_pid.reset_goal(btargetx)
hy_pid.reset_goal(btargety)
for i in range(int(0.1 * self.N)):
#self.sim.data.ctrl[zp_aid], self.sim.data.ctrl[zv_aid] = v_pid.step()
self.sim.data.ctrl[xp_aid], self.sim.data.ctrl[
xv_aid] = hx_pid.step()
self.sim.data.ctrl[yp_aid], self.sim.data.ctrl[
yv_aid] = hy_pid.step()
contact_invisiwall = contact_invisiwall or self._collide_wall()
self.sim.step()
if self.FLAGS['render']:
self.viewer.render()
# Raise the paddle back up after pushing
v_pid.reset_goal(1.5)
for i in range(self.N * 3):
self.sim.data.ctrl[zp_aid], self.sim.data.ctrl[
zv_aid] = v_pid.step()
self.sim.data.ctrl[xp_aid], self.sim.data.ctrl[
xv_aid] = hx_pid.step()
self.sim.data.ctrl[yp_aid], self.sim.data.ctrl[
yv_aid] = hy_pid.step()
contact_invisiwall = contact_invisiwall or self._collide_wall()
self.sim.step()
if self.FLAGS['render']:
self.viewer.render()
if self.FLAGS['penalize_invisiwall'] and contact_invisiwall:
safe = 'wall'
else:
safe = ''
return safe
def reset(self):
self.goal = self._sample_goal()
did_reset_sim = False
while not did_reset_sim:
did_reset_sim = self._reset_sim(
reset_mode=self.np_random.choice(self.FLAGS['reset_mode']))
curr = self._get_obs()['array']
if self.FLAGS['render'] and self.FLAGS['goal_conditioned']:
goal_array = self.goal['array'] * 0.5 * TABLES[
self.FLAGS['default_table']]['wood'][:2]
site_dests = np.c_[goal_array, curr[:, 2]] + self.table_center()
for i in range(self.FLAGS['num_objects']):
site = self.name2sid('object{}'.format(i))
if self.FLAGS['render']:
self.model.site_pos[site] = site_dests[i] - [0.0, 0.0, 0.1]
self.model.site_rgba[site] = np.ones(4)
return self.get_out_obs()
def _collide_wall(self):
wall_gids = [self.name2gid('invisiwall_' + str(i)) for i in range(4)]
obj_gids = [
self.name2gid('object' + str(i))
for i in range(self.FLAGS['num_objects'])
]
bad_contacts = [
1 for cc in self.sim.data.contact
if (cc.geom1 in wall_gids and cc.geom2 in obj_gids) or (
cc.geom1 in obj_gids and cc.geom2 in wall_gids)
]
return len(bad_contacts) > 0
def _set_invisiwall(self):
# NOTE: Generally changing the shape of objects does not do well for contacts
X = self.FLAGS['ACTS']['x']
Y = self.FLAGS['ACTS']['y']
DEPTH, Z = self.model.geom_size[self.name2gid('invisiwall_0')][1:]
Z -= (Z / 2)
idx = 0
for dir in [0, -1]:
for xy in ['x', 'y']:
gid = self.name2gid('invisiwall_' + str(idx))
if self.FLAGS['view_invisiwall']:
pass
else:
self.model.geom_rgba[gid] = np.zeros((4, ))
if xy == 'x':
yval = 1.05 * np.sign(Y[dir]) * (np.abs(Y[dir]) + DEPTH)
self.model.geom_pos[gid] = self.table_center() + np.array(
[0, yval, Z])
#self.model.geom_size[gid] = np.array([X, DEPTH, Z])
else:
xval = 1.05 * np.sign(X[dir]) * (np.abs(X[dir]) + DEPTH)
self.model.geom_pos[gid] = self.table_center() + np.array(
[xval, 0, Z])
#self.model.geom_size[gid] = np.array([DEPTH, Y, Z])
idx += 1
self.sim.forward()
self.sim.step()
def _sample_xyzs(self, reset_mode=None):
reset_mode = reset_mode or 'cluster' #self.FLAGS['reset_mode']
object_xyzs = [] # for checking collisions against
sample_range = R3D(self.FLAGS['obj_rangex'], self.FLAGS['obj_rangey'],
R(0, 0))
if reset_mode == 'uniform':
for i in range(self.FLAGS['num_objects']):
while True:
object_xyz = sim_utils.sample_xyz(self.np_random,
sample_range)
collision = False
# TODO: check if this is necessary. could get speedup
for other in object_xyzs:
collision = collision or np.linalg.norm(
object_xyz[:2] - other[:2]) < 0.0127
if not collision and sim_utils.in_range(
object_xyz[0],
self.FLAGS['obj_rangex']) and sim_utils.in_range(
object_xyz[1], self.FLAGS['obj_rangey']):
object_xyzs.append(object_xyz)
break
elif reset_mode == 'cluster':
diameter = self.model.geom_size[self.name2gid('object0')][0]
dirs = list(
itertools.product([-diameter * 2, 0.0, diameter * 2],
[-diameter * 2, 0.0, diameter * 2]))
first_object_xyz = sim_utils.sample_xyz(self.np_random,
sample_range)
object_xyzs.append(first_object_xyz)
for i in range(1, self.FLAGS['num_objects']):
while True:
reference = object_xyzs[self.np_random.randint(
len(object_xyzs))]
dir = np.array(dirs[self.np_random.randint(len(dirs))])
dir *= self.np_random.uniform(1.0, 2.0)
xyz = reference.copy()
xyz[:2] = reference[:2] + dir
collision = False
for other in object_xyzs:
collision = collision or np.linalg.norm(
xyz[:2] - other[:2]) < 0.0127
if not collision and sim_utils.in_range(
xyz[0],
self.FLAGS['obj_rangex']) and sim_utils.in_range(
xyz[1], self.FLAGS['obj_rangey']):
object_xyzs.append(xyz)
break
return object_xyzs
def _sample_object_poses(self, reset_mode=None):
#rstate = np.random.RandomState(seed=0) # use this to always reset to deterministic state
xyz = self._sample_xyzs(reset_mode=reset_mode)
for i in range(self.FLAGS['num_objects']):
gid = self.name2gid('object{}'.format(i))
#self.model.geom_size[gid] = self.FLAGS['cube_size']
obj = 'object{}:joint'.format(i)
object_qpos = self.sim.data.get_joint_qpos(obj)
assert object_qpos.shape == (7, )
object_xpos = self.table_center() + [0.0, 0.0, 0.2] + xyz[i]
object_quat = sim_utils.sample_quat(
self.np_random, R3D(R(0, 180), R(0, 0), R(0, 0)))
object_qpos = np.concatenate([object_xpos, object_quat])
self.sim.data.set_joint_qpos(obj, object_qpos)
def _reset_sim(self, reset_mode=None):
self.object_has_fallen = False
self.object_fallen_stuff = None
self.sim.set_state(self.initial_state)
if self.FLAGS['baxter']:
set_table(self.model, 'object_table', 'folding', self.FLAGS)
else:
set_table(self.model, 'robot_table', 'robot', self.FLAGS)
set_table(self.model, 'object_table', 'small', self.FLAGS)
self.model.site_size[self.name2sid('target0')] = self.model.geom_size[
self.paddle_gid]
self.cam_pos = self.model.cam_pos[0] = self.table_center() + np.array(
[-0.75, 0.0, 0.75])
target_pos = self.sim.data.body_xpos[self.name2bid('object_table')]
self.model.cam_quat[0] = look_at(self.cam_pos, target_pos)
# reset objects
self._sample_object_poses(reset_mode=reset_mode)
for i in range(5 * self.N):
self.sim.step()
if self.FLAGS['render']:
self.viewer.render()
return not self._object_fell() and not self._geofence_escape()
def _sample_goal(self):
"""NOTE: must be called before reset, as this can mess with state"""
goal = {}
if self.FLAGS['goal_conditioned']:
# Same mechanism for resetting sim, in order to get to a random state
did_reset_sim = False
while not did_reset_sim:
did_reset_sim = self._reset_sim(reset_mode='cluster')
obs = self._get_obs()
obs['array'][:, :3] /= (
0.5 * TABLES[self.FLAGS['default_table']]['wood'])
if not self.FLAGS['use_quat']:
obs['array'] = obs['array'][..., :2]
goal['array'] = copy.deepcopy(obs['array'])
if self.FLAGS['use_image']:
goal['image'] = copy.deepcopy(obs['image'])
return goal
else:
sample_range = R3D(self.FLAGS['obj_rangex'],
self.FLAGS['obj_rangey'], R(0, 0))
goal['array'] = sim_utils.sample_xyz(self.np_random, sample_range)
return goal
def get_out_obs(self):
"""This matches the observation space. The other one is just for internal use"""
obs = self._get_obs()
obs['array'][:, :3] /= (0.5 *
TABLES[self.FLAGS['default_table']]['wood'])
if not self.FLAGS['use_quat']:
obs['array'] = obs['array'][..., :2]
obs_names = ['array', 'single']
if self.FLAGS['goal_conditioned']:
assert not np.all(np.equal(obs['goal_array'],
0.0)), "goal was not set before out_obs"
obs_names.append('goal_array')
if self.FLAGS['use_image']:
obs_names.append('goal_image')
if self.FLAGS['use_image']:
obs_names.append('image')
if self.FLAGS['use_canonical']:
obs_names.append('canonical')
self._canonicalize()
self.sim.step()
canon_obs = self._get_obs()
obs['canonical'] = canon_obs['image']
for key in obs:
obs[key] = obs[key].astype(np.float32)
out_obs = copy.deepcopy(tuple([obs[key] for key in sorted(obs_names)]))
#print(obs['array'])
#print()
if np.max(np.abs(obs['array'])) > 1.01:
print('AHHH out obs', obs['array'])
return out_obs
def _canonicalize(self):
# Canonicalize textures
for name in self.model.geom_names + ('skybox', ):
if re.match('object\d', name) is not None:
self.tex_modder.set_rgb(
name, np.array([255, 0, 255], dtype=np.uint8))
else:
self.tex_modder.set_rgb(name,
np.array([255] * 3, dtype=np.uint8))
# light
for key in self.LIGHT:
self.LIGHT[key][:] = self.START_LIGHT[key].copy()
# camera
if self.FLAGS['mild_canonical']:
self.cam_pos = self.CAMERA_POS_MEAN + self.table_center()
else:
self.cam_pos = self.CAMERA_POS_MEAN + self.table_center() + [
0.639, -0.025, 0.125
]
target_id = self.model.body_name2id('object_table')
target_pos = self.sim.data.body_xpos[
target_id] #+ sim_utils.sample_xyz(self.np_random, R3D(R(-0.05, 0.05), R(-0.05, 0.05), R(0.1,0.1)))
quat = look_at(self.cam_pos, target_pos)
self.cam_modder.set_quat('camera1', quat)
self.cam_modder.set_pos('camera1', self.cam_pos)
self.cam_modder.set_fovy('camera1', 45)
# robot
if self.FLAGS['baxter']:
for name in self.model.joint_names:
if 'paddle' not in name and 'object' not in name:
id = self.sim.model.joint_name2id(name)
self.sim.data.set_joint_qpos(name, 0.0)
#self.model.body_pos[self.name2bid('base')] = self.START_BODY_POS[self.name2bid('base')]
else:
for joint in ['lbr4_j{}'.format(i) for i in range(7)]:
id = self.sim.model.joint_name2id(joint)
self.sim.data.set_joint_qpos(joint, 0.0)
PREFIX = 'distract'
geom_names = [
name for name in self.model.geom_names if name.startswith(PREFIX)
]
for name in geom_names:
gid = self.model.geom_name2id(name)
self.model.geom_pos[gid] = np.array([0, 0, -2])
self.model.geom_rgba[gid][-1] = 0
def _randomize(self):
if self.FLAGS['domrand']:
self._rand_textures()
self._rand_camera()
self._rand_lights()
#self._rand_robot()
self._rand_distract()
else:
self._canonicalize()
def _rand_textures(self):
"""Randomize all the textures in the scene, including the skybox"""
bright = self.np_random.binomial(1, 0.8)
for name in self.sim.model.geom_names + ('skybox', ):
self.tex_modder.rand_all(name)
if bright:
if name == 'object_table':
self.tex_modder.brighten(name,
self.np_random.randint(150, 255))
else:
self.tex_modder.brighten(name,
self.np_random.randint(0, 150))
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/
"""
dx = 0.05
self.cam_pos = self.CAMERA_POS_MEAN + self.table_center()
C_R3D = R3D(R(-dx, dx), R(-dx, dx), R(-2 * dx, 2 * dx))
self.cam_pos += sim_utils.sample_xyz(self.np_random, C_R3D)
self._rand_camera_angle()
self.cam_modder.set_pos('camera1', self.cam_pos)
self.cam_modder.set_fovy('camera1',
sim_utils.sample(self.np_random, R(44, 46)))
def _rand_camera_angle(self):
# Look approximately at the robot
target_id = self.model.body_name2id('object_table')
target_pos = self.sim.data.body_xpos[target_id] + sim_utils.sample_xyz(
self.np_random, R3D(R(-0.02, 0.02), R(-0.02, 0.02), R(0.0, 0.2)))
quat = look_at(self.cam_pos, target_pos)
self.cam_modder.set_quat('camera1', quat)
def _rand_lights(self):
"""Randomize pos, direction, and lights"""
# light stuff
X = R(-1.0, 1.0)
Y = R(-0.6, 0.6)
Z = R(0.1, 1.5)
LIGHT_R3D = self.table_center()[:, None] + R3D(X, Y, Z)
LIGHT_UNIF = R3D(R(0, 1), R(0, 1), R(0, 1))
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,
sim_utils.sample(self.np_random, [0, 1]) < 0.8)
self.light_modder.set_dir(
name, sim_utils.sample_light_dir(self.np_random))
self.light_modder.set_pos(
name, sim_utils.sample_xyz(self.np_random, LIGHT_R3D))
spec = np.array([sim_utils.sample(self.np_random, R(0.5, 1))] * 3)
diffuse = np.array([sim_utils.sample(self.np_random, R(0.5, 1))] *
3)
ambient = np.array([sim_utils.sample(self.np_random, R(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_castshadow[lid] = sim_utils.sample(
self.np_random, [0, 1]) < 0.5
def _rand_robot(self):
"""Randomize joint angles"""
if self.FLAGS['baxter']:
for name in self.model.joint_names:
if 'paddle' not in name and 'object' not in name:
id = self.sim.model.joint_name2id(name)
self.sim.data.set_joint_qpos(
name,
sim_utils.sample(self.np_random,
self.model.jnt_range[id]))
self.model.body_pos[self.name2bid('base')] = self.START_BODY_POS[
self.name2bid('base')] + sim_utils.sample_xyz(
self.np_random, R3D(R(0, 0), R(0, 0), R(-0.05, 0.05)))
else:
for name in ['lbr4_j{}'.format(i) for i in range(7)]:
id = self.sim.model.joint_name2id(name)
self.sim.data.set_joint_qpos(
name,
sim_utils.sample(self.np_random, self.model.jnt_range[id]))
def _rand_distract(self):
"""Randomize the position and size of the distractor objects"""
PREFIX = 'distract'
geom_names = [
name for name in self.model.geom_names if name.startswith(PREFIX)
]
# Size range
SX = R(0.01, 0.3)
SY = R(0.01, 0.3)
SZ = R(0.01, 0.3)
S3D = R3D(SX, SY, SZ)
# Back range
B_PX = R(0.5, 1.0)
B_PY = R(-2, 2)
B_PZ = R(0.1, 0.5)
B_P3D = R3D(B_PX, B_PY, B_PZ)
# Front range
F_PX = R(-0.5, 0.5)
F_PY = R(-2, 2)
F_PZ = R(-0.1, 0.3)
F_P3D = R3D(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
mid = self.table_center().copy()
mid[2] = -0.925
self.model.geom_pos[gid] = mid + sim_utils.sample_xyz(
self.np_random, range)
self.model.geom_quat[gid] = sim_utils.random_quat(self.np_random)
self.model.geom_size[gid] = sim_utils.sample_xyz(
self.np_random, S3D)
self.model.geom_type[gid] = sim_utils.sample_geom_type(
self.np_random)
self.model.geom_rgba[gid][-1] = np.random.binomial(1, 0.5)
class RobotEnv(gym.GoalEnv):
def __init__(self, model_path, initial_qpos, n_actions, n_substeps, mode, visual_randomize, visual_data_recording):
if model_path.startswith('/'):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), 'assets', model_path)
if not os.path.exists(fullpath):
raise IOError('File {} does not exist'.format(fullpath))
self.mode = mode
model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(model, nsubsteps=n_substeps)
self.viewer = None # comment when using "human"
self._viewers = {}
self.modder = TextureModder(self.sim)
self.visual_randomize = visual_randomize
self.mat_modder = MaterialModder(self.sim)
self.light_modder = LightModder(self.sim)
self.camera_modder = CameraModder(self.sim)
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self.visual_data_recording = visual_data_recording
self._index = 0
self._label_matrix = []
self.seed()
self._env_setup(initial_qpos=initial_qpos)
self.initial_state = copy.deepcopy(self.sim.get_state())
self.goal = self._sample_goal()
obs = self._get_obs()
self.action_space = spaces.Box(-1., 1., shape=(n_actions,), dtype='float32')
self.observation_space = spaces.Dict(dict(
desired_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'),
achieved_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'),
observation=spaces.Box(-np.inf, np.inf, shape=obs['observation'].shape, dtype='float32'),
))
if self.viewer:
self._viewer_setup()
@property
def dt(self):
return self.sim.model.opt.timestep * self.sim.nsubsteps
# Env methods
# ----------------------------
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
if self.visual_randomize:
for name in self.sim.model.geom_names:
self.modder.whiten_materials()
self.modder.set_checker(name, (255, 0, 0), (0, 0, 0))
self.modder.rand_all(name)
self.mat_modder.rand_all(name)
self.modder.set_checker('skin', (255, 0, 0), (0, 0, 0))
self.modder.rand_all('skin')
self.light_modder.rand_all('light0')
self.light_modder.rand_all('light1')
self.light_modder.rand_all('light2')
self.light_modder.rand_all('light3')
self.camera_modder.rand_fovy('camera1')
self.camera_modder.rand_pos('camera1')
self.camera_modder.rand_quat('camera1', factor=[0.1, 0.1, 0.1])
action = np.clip(action, self.action_space.low, self.action_space.high)
self._set_action(action)
self.sim.step()
self._step_callback()
obs = self._get_obs()
done = False
info = {
'is_success': self._is_success(obs['achieved_goal'], self.goal),
}
reward = self.compute_reward(obs['achieved_goal'], self.goal, info)
#print("REWARD", reward ," and IS SUCCESS ", info['is_success'])
return obs, reward, done, info
def reset(self):
# Attempt to reset the simulator. Since we randomize initial conditions, it
# is possible to get into a state with numerical issues (e.g. due to penetration or
# Gimbel lock) or we may not achieve an initial condition (e.g. an object is within the hand).
# In this case, we just keep randomizing until we eventually achieve a valid initial
# configuration.
super(RobotEnv, self).reset()
did_reset_sim = False
while not did_reset_sim:
did_reset_sim = self._reset_sim()
# if self.visual_randomize:
# for name in self.sim.model.geom_names:
# self.modder.whiten_materials()
# self.modder.set_checker(name, (255, 0, 0), (0, 0, 0))
# self.modder.rand_all(name)
# self.mat_modder.rand_all(name)
# self.modder.set_checker('skin', (255, 0, 0), (0, 0, 0))
# self.modder.rand_all('skin')
self.goal = self._sample_goal().copy()
obs = self._get_obs()
return obs
def close(self):
if self.viewer is not None:
# self.viewer.finish()
self.viewer = None
self._viewers = {}
def render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE):
self._render_callback()
if mode == 'rgb_array':
self._get_viewer(mode).render(width, height)
# window size used for old mujoco-py:
data = self._get_viewer(mode).read_pixels(width, height, depth=False)
# original image is upside-down, so flip it
return data[::-1, :, :]
#return None
elif mode == 'human':
self._get_viewer(mode).render()
def _get_viewer(self, mode):
self.viewer = self._viewers.get(mode)
if self.viewer is None:
if mode == 'human':
self.viewer = mujoco_py.MjViewer(self.sim)
elif mode == 'rgb_array':
device_id = self.sim.model.camera_name2id('camera1')
self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, device_id=device_id)
self._viewer_setup()
self._viewers[mode] = self.viewer
return self.viewer
# Extension methods
# ----------------------------
def _reset_sim(self):
"""Resets a simulation and indicates whether or not it was successful.
If a reset was unsuccessful (e.g. if a randomized state caused an error in the
simulation), this method should indicate such a failure by returning False.
In such a case, this method will be called again to attempt a the reset again.
"""
self.sim.set_state(self.initial_state)
self.sim.forward()
return True
def _get_obs(self):
"""Returns the observation.
"""
raise NotImplementedError()
def _set_action(self, action):
"""Applies the given action to the simulation.
"""
raise NotImplementedError()
def _is_success(self, achieved_goal, desired_goal):
"""Indicates whether or not the achieved goal successfully achieved the desired goal.
"""
raise NotImplementedError()
def _sample_goal(self):
"""Samples a new goal and returns it.
"""
raise NotImplementedError()
def _env_setup(self, initial_qpos):
"""Initial configuration of the environment. Can be used to configure initial state
and extract information from the simulation.
"""
pass
def _viewer_setup(self):
"""Initial configuration of the viewer. Can be used to set the camera position,
for example.
"""
pass
def _render_callback(self):
"""A custom callback that is called before rendering. Can be used
to implement custom visualizations.
"""
pass
def _step_callback(self):
"""A custom callback that is called after stepping the simulation. Can be used
to enforce additional constraints on the simulation state.
"""
pass
