def __init__(self, env):
"""
Wrap the environment in this wrapper, that will make the observation egocentric
:param env object: the environment to wrap.
"""
super(EgocentricCostmap, self).__init__(env)
# As openai gym style requires knowing resolution of the image up front
self._egomap_x_bounds = np.array([-0.5, 3.
]) # aligned with robot's direction
self._egomap_y_bounds = np.array([-2., 2.
]) # orthogonal to robot's direction
resulting_size = np.array([
self._egomap_x_bounds[1] - self._egomap_x_bounds[0],
self._egomap_y_bounds[1] - self._egomap_y_bounds[0]
])
pixel_size = world_to_pixel(resulting_size,
np.zeros((2, )),
resolution=0.03)
data_shape = (pixel_size[1], pixel_size[0], 1)
self.observation_space = spaces.Dict(
OrderedDict((('env',
spaces.Box(low=0,
high=255,
shape=data_shape,
dtype=np.uint8)),
('goal',
spaces.Box(low=-1.,
high=1.,
shape=(3, 1),
dtype=np.float64)))))
def __init__(self):
super(ColoredEgoCostmapRandomAisleTurnEnv, self).__init__()
# TODO: Will need some trickery to do it fully openai gym style
# As openai gym style requires knowing resolution of the image up front
self._egomap_x_bounds = np.array([-0.5, 3.5
]) # aligned with robot's direction
self._egomap_y_bounds = np.array([-2., 2.
]) # orthogonal to robot's direction
resulting_size = (self._egomap_x_bounds[1] - self._egomap_x_bounds[0],
self._egomap_y_bounds[1] - self._egomap_y_bounds[0])
pixel_size = world_to_pixel(np.asarray(resulting_size,
dtype=np.float64),
np.zeros((2, )),
resolution=0.03)
data_shape = (pixel_size[1], pixel_size[0], 1)
self.observation_space = spaces.Dict(
OrderedDict((('environment',
spaces.Box(low=0,
high=255,
shape=data_shape,
dtype=np.uint8)),
('goal',
spaces.Box(low=-1.,
high=1.,
shape=(5, 1),
dtype=np.float64)))))