def __init__(self, config, gpu_idx=0):
self.config = self.parse_config(config)
print(self.config["envname"])
assert (self.config["envname"] == self.__class__.__name__
or self.config["envname"] == "TestEnv")
CameraRobotEnv.__init__(self,
self.config,
gpu_idx,
scene_type="building",
tracking_camera=tracking_camera)
self.robot_introduce(Husky(self.config, env=self))
self.scene_introduce()
self.total_reward = 0
self.total_frame = 0
self.flag_timeout = 1
self.visualid = -1
self.lastid = None
self.gui = self.config["mode"] == "gui"
if self.gui:
self.visualid = p.createVisualShape(
p.GEOM_MESH,
fileName=os.path.join(pybullet_data.getDataPath(), 'cube.obj'),
meshScale=[0.2, 0.2, 0.2],
rgbaColor=[1, 0, 0, 0.7])
self.colisionid = p.createCollisionShape(
p.GEOM_MESH,
fileName=os.path.join(pybullet_data.getDataPath(), 'cube.obj'),
meshScale=[0.2, 0.2, 0.2])
self.lastid = None
self.obstacle_dist = 100
def __init__(self, config, gpu_idx=0):
self.config = self.parse_config(config)
print(self.config["envname"])
assert(self.config["envname"] == self.__class__.__name__ or self.config["envname"] == "TestEnv")
CameraRobotEnv.__init__(self, self.config, gpu_idx,
scene_type="building",
tracking_camera=tracking_camera)
self.robot_introduce(Humanoid(self.config, env=self))
self.scene_introduce()
self.gui = self.config["mode"] == "gui"
self.total_reward = 0
self.total_frame = 0
def __init__(self, config, gpu_idx=0):
self.config = self.parse_config(config)
assert (self.config["envname"] == self.__class__.__name__
or self.config["envname"] == "TestEnv")
CameraRobotEnv.__init__(self,
self.config,
gpu_idx,
scene_type="stadium" if self.config["model_id"]
== "stadium" else "building",
tracking_camera=tracking_camera)
self.robot_introduce(JR2(self.config, env=self))
self.scene_introduce()
self.total_reward = 0
self.total_frame = 0
def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0:
self._flag_reposition()
self.flag_timeout -= 1
return state, reward, done, meta
def _step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: The angles, velocities and torques of all motors.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
"""
#print("Env apply raw action", action)
action = self._transform_action_to_motor_command(action)
#print("Env apply action", action)
#for _ in range(self._action_repeat):
# self.robot.ApplyAction(action)
# pybullet.stepSimulation()
for i in range(len(self.Amax)):
if action[i] > self.Amax[i]:
self.Amax[i] = action[i]
#print("Action max", self.Amax)
for _ in range(self.action_repeat):
state = CameraRobotEnv._step(self, action)
return state
def _reset(self):
self.total_frame = 0
self.total_reward = 0
self._randomize_target()
self._flag_reposition()
obs = CameraRobotEnv._reset(
self) ## Important: must come after flat_reposition
return obs
def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0 or (self.flag_timeout < 225
and self.robot.walk_target_dist < 0.8):
self._flag_reposition()
self.flag_timeout -= 1
if "depth" in self.config["output"]:
depth_obs = self.get_observations()["depth"]
x_start = int(self.windowsz / 2 - 16)
x_end = int(self.windowsz / 2 + 16)
y_start = int(self.windowsz / 2 - 16)
y_end = int(self.windowsz / 2 + 16)
self.obstacle_dist = (np.mean(depth_obs[x_start:x_end,
y_start:y_end, -1]))
return state, reward, done, meta
def _reset(self):
self.total_frame = 0
self.total_reward = 0
obs = CameraRobotEnv._reset(self)
self._flag_reposition()
return obs
def _reset(self):
CameraRobotEnv._reset(self)
for flagId in self.semantic_flagIds:
p.changeVisualShape(flagId, -1, rgbaColor=[1, 0, 0, 1])
def _reset(self):
obs = CameraRobotEnv._reset(self)
return obs
def __init__(self, config, gpu_idx=0):
"""Initialize the minitaur gym environment.
Args:
distance_weight: The weight of the distance term in the reward.
energy_weight: The weight of the energy term in the reward.
shake_weight: The weight of the vertical shakiness term in the reward.
drift_weight: The weight of the sideways drift term in the reward.
distance_limit: The maximum distance to terminate the episode.
observation_noise_stdev: The standard deviation of observation noise.
leg_model_enabled: Whether to use a leg motor to reparameterize the action
space.
hard_reset: Whether to wipe the simulation and load everything when reset
is called. If set to false, reset just place the minitaur back to start
position and set its pose to initial configuration.
env_randomizer: An EnvRandomizer to randomize the physical properties
during reset().
"""
self.config = self.parse_config(config)
assert (self.config["envname"] == self.__class__.__name__
or self.config["envname"] == "TestEnv")
CameraRobotEnv.__init__(self,
self.config,
gpu_idx,
scene_type="building",
tracking_camera=tracking_camera)
self.robot_introduce(
Minitaur(self.config,
env=self,
pd_control_enabled=self.pd_control_enabled,
accurate_motor_model_enabled=self.
accurate_motor_model_enabled))
self.scene_introduce()
self.gui = self.config["mode"] == "gui"
self.total_reward = 0
self.total_frame = 0
self.action_repeat = 1
## Important: PD controller needs more accuracy
'''if self.pd_control_enabled or self.accurate_motor_model_enabled:
self.time_step = self.config["speed"]["timestep"]
self.time_step /= self.NUM_SUBSTEPS
self.num_bullet_solver_iterations /= self.NUM_SUBSTEPS
self.action_repeat *= self.NUM_SUBSTEPS
pybullet.setPhysicsEngineParameter(physicsClientId=self.physicsClientId,
numSolverIterations=int(self.num_bullet_solver_iterations))
pybullet.setTimeStep(self.time_step, physicsClientId=self.physicsClientId)
'''
pybullet.setPhysicsEngineParameter(
physicsClientId=self.physicsClientId,
numSolverIterations=int(self.num_bullet_solver_iterations))
self._observation = []
self._last_base_position = [0, 0, 0]
self._action_bound = self.action_bound
self._env_randomizer = self.env_randomizer
if self._env_randomizer is not None:
self._env_randomizer.randomize_env(self)
self._objectives = []
self.viewer = None
self.Amax = [0] * 8
def _reset(self):
self.total_frame = 0
self.total_reward = 0
obs = CameraRobotEnv._reset(self)
return obs