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minitaur gym environment with bunny on its back
"""This file implements the gym environment of minitaur.
"""
import os, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
os.sys.path.insert(0,parentdir)
import math
import time
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import pybullet
from . import bullet_client
from . import minitaur
import os
import pybullet_data
from . import minitaur_env_randomizer
NUM_SUBSTEPS = 5
NUM_MOTORS = 8
MOTOR_ANGLE_OBSERVATION_INDEX = 0
MOTOR_VELOCITY_OBSERVATION_INDEX = MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS
MOTOR_TORQUE_OBSERVATION_INDEX = MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS
BASE_ORIENTATION_OBSERVATION_INDEX = MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS
ACTION_EPS = 0.01
OBSERVATION_EPS = 0.01
RENDER_HEIGHT = 720
RENDER_WIDTH = 960
duckStartPos = [0,0,0.25]
duckStartOrn = [0.5,0.5,0.5,0.5]
class MinitaurBulletEnv(gym.Env):
"""The gym environment for the minitaur.
It simulates the locomotion of a minitaur, a quadruped robot. The state space
include the angles, velocities and torques for all the motors and the action
space is the desired motor angle for each motor. The reward function is based
on how far the minitaur walks in 1000 steps and penalizes the energy
expenditure.
"""
metadata = {
"render.modes": ["human", "rgb_array"],
"video.frames_per_second": 50
}
def __init__(self,
urdf_root=pybullet_data.getDataPath(),
action_repeat=1,
distance_weight=1.0,
energy_weight=0.005,
shake_weight=0.0,
drift_weight=0.0,
distance_limit=float("inf"),
observation_noise_stdev=0.0,
self_collision_enabled=True,
motor_velocity_limit=np.inf,
pd_control_enabled=False,#not needed to be true if accurate motor model is enabled (has its own better PD)
leg_model_enabled=True,
accurate_motor_model_enabled=True,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=True,
hard_reset=True,
on_rack=False,
render=False,
kd_for_pd_controllers=0.3,
env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
"""Initialize the minitaur gym environment.
Args:
urdf_root: The path to the urdf data folder.
action_repeat: The number of simulation steps before actions are applied.
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.
self_collision_enabled: Whether to enable self collision in the sim.
motor_velocity_limit: The velocity limit of each motor.
pd_control_enabled: Whether to use PD controller for each motor.
leg_model_enabled: Whether to use a leg motor to reparameterize the action
space.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model.
motor_kd: derivative gain for the accurate motor model.
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
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.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
render: Whether to render the simulation.
kd_for_pd_controllers: kd value for the pd controllers of the motors
env_randomizer: An EnvRandomizer to randomize the physical properties
during reset().
"""
self._time_step = 0.01
self._action_repeat = action_repeat
self._num_bullet_solver_iterations = 300
self._urdf_root = urdf_root
self._self_collision_enabled = self_collision_enabled
self._motor_velocity_limit = motor_velocity_limit
self._observation = []
self._env_step_counter = 0
self._is_render = render
self._last_base_position = [0, 0, 0]
self._distance_weight = distance_weight
self._energy_weight = energy_weight
self._drift_weight = drift_weight
self._shake_weight = shake_weight
self._distance_limit = distance_limit
self._observation_noise_stdev = observation_noise_stdev
self._action_bound = 1
self._pd_control_enabled = pd_control_enabled
self._leg_model_enabled = leg_model_enabled
self._accurate_motor_model_enabled = accurate_motor_model_enabled
self._motor_kp = motor_kp
self._motor_kd = motor_kd
self._torque_control_enabled = torque_control_enabled
self._motor_overheat_protection = motor_overheat_protection
self._on_rack = on_rack
self._cam_dist = 1.0
self._cam_yaw = 0
self._duckId = -1
self._cam_pitch = -30
self._hard_reset = True
self._kd_for_pd_controllers = kd_for_pd_controllers
self._last_frame_time = 0.0
print("urdf_root=" + self._urdf_root)
self._env_randomizer = env_randomizer
# PD control needs smaller time step for stability.
if pd_control_enabled or accurate_motor_model_enabled:
self._time_step /= NUM_SUBSTEPS
self._num_bullet_solver_iterations /= NUM_SUBSTEPS
self._action_repeat *= NUM_SUBSTEPS
if self._is_render:
self._pybullet_client = bullet_client.BulletClient(
connection_mode=pybullet.GUI)
else:
self._pybullet_client = bullet_client.BulletClient()
self._seed()
self.reset()
observation_high = (
self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
observation_low = (
self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
action_dim = 8
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(observation_low, observation_high)
self.viewer = None
self._hard_reset = hard_reset # This assignment need to be after reset()
def set_env_randomizer(self, env_randomizer):
self._env_randomizer = env_randomizer
def configure(self, args):
self._args = args
def _reset(self):
if self._hard_reset:
self._pybullet_client.resetSimulation()
self._pybullet_client.setPhysicsEngineParameter(
numSolverIterations=int(self._num_bullet_solver_iterations))
self._pybullet_client.setTimeStep(self._time_step)
self._groundId = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root,duckStartPos,duckStartOrn)
self._pybullet_client.setGravity(0, 0, -10)
acc_motor = self._accurate_motor_model_enabled
motor_protect = self._motor_overheat_protection
self.minitaur = (minitaur.Minitaur(
pybullet_client=self._pybullet_client,
urdf_root=self._urdf_root,
time_step=self._time_step,
self_collision_enabled=self._self_collision_enabled,
motor_velocity_limit=self._motor_velocity_limit,
pd_control_enabled=self._pd_control_enabled,
accurate_motor_model_enabled=acc_motor,
motor_kp=self._motor_kp,
motor_kd=self._motor_kd,
torque_control_enabled=self._torque_control_enabled,
motor_overheat_protection=motor_protect,
on_rack=self._on_rack,
kd_for_pd_controllers=self._kd_for_pd_controllers))
else:
self.minitaur.Reset(reload_urdf=False)
self._pybullet_client.resetBasePositionAndOrientation(self._duckId,duckStartPos,duckStartOrn)
if self._env_randomizer is not None:
self._env_randomizer.randomize_env(self)
self._env_step_counter = 0
self._last_base_position = [0, 0, 0]
self._objectives = []
self._pybullet_client.resetDebugVisualizerCamera(
self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0])
if not self._torque_control_enabled:
for _ in range(100):
if self._pd_control_enabled or self._accurate_motor_model_enabled:
self.minitaur.ApplyAction([math.pi / 2] * 8)
self._pybullet_client.stepSimulation()
return self._noisy_observation()
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def _transform_action_to_motor_command(self, action):
if self._leg_model_enabled:
for i, action_component in enumerate(action):
if not (-self._action_bound - ACTION_EPS <= action_component <=
self._action_bound + ACTION_EPS):
raise ValueError(
"{}th action {} out of bounds.".format(i, action_component))
action = self.minitaur.ConvertFromLegModel(action)
return action
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.
"""
if self._is_render:
# Sleep, otherwise the computation takes less time than real time,
# which will make the visualization like a fast-forward video.
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self._action_repeat * self._time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetBasePosition()
self._pybullet_client.resetDebugVisualizerCamera(
self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos)
action = self._transform_action_to_motor_command(action)
for _ in range(self._action_repeat):
self.minitaur.ApplyAction(action)
self._pybullet_client.stepSimulation()
self._env_step_counter += 1
reward = self._reward()
done = self._termination()
return np.array(self._noisy_observation()), reward, done, {}
def _render(self, mode="rgb_array", close=False):
if mode != "rgb_array":
return np.array([])
base_pos = self.minitaur.GetBasePosition()
view_matrix = self._pybullet_client.computeViewMatrixFromYawPitchRoll(
cameraTargetPosition=base_pos,
distance=self._cam_dist,
yaw=self._cam_yaw,
pitch=self._cam_pitch,
roll=0,
upAxisIndex=2)
proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(
fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
nearVal=0.1, farVal=100.0)
(_, _, px, _, _) = self._pybullet_client.getCameraImage(
width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
rgb_array = np.array(px)
rgb_array = rgb_array[:, :, :3]
return rgb_array
def get_minitaur_motor_angles(self):
"""Get the minitaur's motor angles.
Returns:
A numpy array of motor angles.
"""
return np.array(
self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:
MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS])
def get_minitaur_motor_velocities(self):
"""Get the minitaur's motor velocities.
Returns:
A numpy array of motor velocities.
"""
return np.array(
self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:
MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS])
def get_minitaur_motor_torques(self):
"""Get the minitaur's motor torques.
Returns:
A numpy array of motor torques.
"""
return np.array(
self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:
MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS])
def get_minitaur_base_orientation(self):
"""Get the minitaur's base orientation, represented by a quaternion.
Returns:
A numpy array of minitaur's orientation.
"""
return np.array(self._observation[BASE_ORIENTATION_OBSERVATION_INDEX:])
def lost_duck(self):
points = self._pybullet_client.getContactPoints(self._duckId, self._groundId);
return len(points)>0
def is_fallen(self):
"""Decide whether the minitaur has fallen.
If the up directions between the base and the world is larger (the dot
product is smaller than 0.85) or the base is very low on the ground
(the height is smaller than 0.13 meter), the minitaur is considered fallen.
Returns:
Boolean value that indicates whether the minitaur has fallen.
"""
orientation = self.minitaur.GetBaseOrientation()
rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation)
local_up = rot_mat[6:]
pos = self.minitaur.GetBasePosition()
return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or
pos[2] < 0.13)
def _termination(self):
position = self.minitaur.GetBasePosition()
distance = math.sqrt(position[0]**2 + position[1]**2)
return self.lost_duck() or self.is_fallen() or distance > self._distance_limit
def _reward(self):
current_base_position = self.minitaur.GetBasePosition()
forward_reward = current_base_position[0] - self._last_base_position[0]
drift_reward = -abs(current_base_position[1] - self._last_base_position[1])
shake_reward = -abs(current_base_position[2] - self._last_base_position[2])
self._last_base_position = current_base_position
energy_reward = np.abs(
np.dot(self.minitaur.GetMotorTorques(),
self.minitaur.GetMotorVelocities())) * self._time_step
reward = (
self._distance_weight * forward_reward -
self._energy_weight * energy_reward + self._drift_weight * drift_reward
+ self._shake_weight * shake_reward)
self._objectives.append(
[forward_reward, energy_reward, drift_reward, shake_reward])
return reward
def get_objectives(self):
return self._objectives
def _get_observation(self):
self._observation = self.minitaur.GetObservation()
return self._observation
def _noisy_observation(self):
self._get_observation()
observation = np.array(self._observation)
if self._observation_noise_stdev > 0:
observation += (np.random.normal(
scale=self._observation_noise_stdev, size=observation.shape) *
self.minitaur.GetObservationUpperBound())
return observation
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