In this new ROS Project you are going to learn Step-by-Step how to create a moving cube and that it learns to move using OpenAI environment.
This second video is for learning the creation the basics of Reinforcement learning and how to connect to the various systems of the robot to get the state, perform actions and calculate rewards.
If you didn’t follow up, please check the link below for the last post.
[irp posts=”9744″ name=”[ROS Projects] OpenAI with Moving Cube Robot in Gazebo Step-by-Step Part1″]
Step 1. Clone the simulation
In order to make sure we have the same project. Please run the following command
cd ~/simulation_ws/src
git clone https://bitbucket.org/theconstructcore/moving_cube.git
NOTICE: please delete the previous code if you have problems to compile the code
In the /moving_cube/moving_cube_description/urdf/moving_cube.urdf file, please uncomment the following part. We’ll need this part to publish the odom topic.
To train the robot, let’s create a package for training under the catkin_ws/src
cd ~/catkin_ws/src
catkin_create_pkg my_moving_cube_traning_pkg rospy
Then we’ll create a script folder inside the package and put a file called cube_rl_utils.py inside it with the following content
#!/usr/bin/env python
import time
import rospy
import math
import copy
import numpy
from std_msgs.msg import Float64
from sensor_msgs.msg import JointState
from nav_msgs.msg import Odometry
from geometry_msgs.msg import Point
from tf.transformations import euler_from_quaternion
class CubeRLUtils(object):
def __init__(self):
self.check_all_sensors_ready()
rospy.Subscriber("/moving_cube/joint_states", JointState, self.joints_callback)
rospy.Subscriber("/moving_cube/odom", Odometry, self.odom_callback)
self._roll_vel_pub = rospy.Publisher('/moving_cube/inertia_wheel_roll_joint_velocity_controller/command', Float64, queue_size=1)
self.check_publishers_connection()
def check_all_sensors_ready(self):
self.disk_joints_data = None
while self.disk_joints_data is None and not rospy.is_shutdown():
try:
self.disk_joints_data = rospy.wait_for_message("/moving_cube/joint_states", JointState, timeout=1.0)
rospy.loginfo("Current moving_cube/joint_states READY=>"+str(self.disk_joints_data))
except:
rospy.logerr("Current moving_cube/joint_states not ready yet, retrying for getting joint_states")
self.cube_odom_data = None
while self.disk_joints_data is None and not rospy.is_shutdown():
try:
self.cube_odom_data = rospy.wait_for_message("/moving_cube/odom", Odometry, timeout=1.0)
rospy.loginfo("Current /moving_cube/odom READY=>" + str(self.cube_odom_data))
except:
rospy.logerr("Current /moving_cube/odom not ready yet, retrying for getting odom")
rospy.loginfo("ALL SENSORS READY")
def check_publishers_connection(self):
"""
Checks that all the publishers are working
:return:
"""
rate = rospy.Rate(10) # 10hz
while (self._roll_vel_pub.get_num_connections() == 0 and not rospy.is_shutdown()):
rospy.loginfo("No susbribers to _roll_vel_pub yet so we wait and try again")
try:
rate.sleep()
except rospy.ROSInterruptException:
# This is to avoid error when world is rested, time when backwards.
pass
rospy.loginfo("_base_pub Publisher Connected")
rospy.loginfo("All Publishers READY")
def joints_callback(self, data):
self.joints = data
def odom_callback(self, data):
self.odom = data
# Reinforcement Learning Utility Code
def move_joints(self, roll_speed):
joint_speed_value = Float64()
joint_speed_value.data = roll_speed
rospy.loginfo("Single Disk Roll Velocity>>"+str(joint_speed_value))
self._roll_vel_pub.publish(joint_speed_value)
def get_cube_state(self):
# We convert from quaternions to euler
orientation_list = [self.odom.pose.pose.orientation.x,
self.odom.pose.pose.orientation.y,
self.odom.pose.pose.orientation.z,
self.odom.pose.pose.orientation.w]
roll, pitch, yaw = euler_from_quaternion(orientation_list)
# We get the distance from the origin
start_position = Point()
start_position.x = 0.0
start_position.y = 0.0
start_position.z = 0.0
distance = self.get_distance_from_point(start_position,
self.odom.pose.pose.position)
cube_state = [
round(self.joints.velocity[0],1),
round(distance,1),
round(roll,1),
round(pitch,1),
round(yaw,1)
]
return cube_state
def observation_checks(self, cube_state):
# MAximum distance to travel permited in meters from origin
max_distance=2.0
if (cube_state[1] > max_distance):
rospy.logerr("Cube Too Far==>"+str(cube_state[1]))
done = True
else:
rospy.loginfo("Cube NOT Too Far==>"+str(cube_state[1]))
done = False
return done
def get_distance_from_point(self, pstart, p_end):
"""
Given a Vector3 Object, get distance from current position
:param p_end:
:return:
"""
a = numpy.array((pstart.x, pstart.y, pstart.z))
b = numpy.array((p_end.x, p_end.y, p_end.z))
distance = numpy.linalg.norm(a - b)
return distance
def get_reward_for_observations(self, state):
# We reward it for lower speeds and distance traveled
speed = state[0]
distance = state[1]
# Positive Reinforcement
reward_distance = distance * 10.0
# Negative Reinforcement for magnitude of speed
reward_for_efective_movement = -1 * abs(speed)
reward = reward_distance + reward_for_efective_movement
rospy.loginfo("Reward_distance="+str(reward_distance))
rospy.loginfo("Reward_for_efective_movement= "+str(reward_for_efective_movement))
return reward
def cube_rl_systems_test():
rospy.init_node('cube_rl_systems_test_node', anonymous=True, log_level=rospy.INFO)
cube_rl_utils_object = CubeRLUtils()
rospy.loginfo("Moving to Speed==>80")
cube_rl_utils_object.move_joints(roll_speed=80.0)
time.sleep(2)
rospy.loginfo("Moving to Speed==>-80")
cube_rl_utils_object.move_joints(roll_speed=-80.0)
time.sleep(2)
rospy.loginfo("Moving to Speed==>0.0")
cube_rl_utils_object.move_joints(roll_speed=0.0)
time.sleep(2)
cube_state = cube_rl_utils_object.get_cube_state()
done = cube_rl_utils_object.observation_checks(cube_state)
reward = cube_rl_utils_object.get_reward_for_observations(cube_state)
rospy.loginfo("Done==>"+str(done))
rospy.loginfo("Reward==>"+str(reward))
if __name__ == "__main__":
cube_rl_systems_test()
In this post, we’ll focus on the cube_rl_systems_test() function. The function uses the class to move the cube, get the observation, calculate reward and check if it’s done. To run it, you have to run the simulation first. Please go to Simulations->Select launch file-> main.launch
NOTICE: You have to unpause the simulation by clicking the arrow key in the simulation window
Then you can run the following command to run the script
cd ~/catkin_ws/src/my_moving_cube_training_pkg/script
chmod +x cube_rl_utils.py
cd ~/catkin_ws
source devel_setup.bash
rosrun my_moving_cube_training_pkg cube_rl_utils.py
You should see the cube moving around and the reward and the done state is calculated.
Edit by: Tony Huang
[irp posts=”9976″ name=”ROS Projects OpenAI with Moving Cube Robot in Gazebo Step-by-Step Part3″]
Step 1. Create a project in ROS Development Studio(ROSDS)
We’ll use ROSDS through this project in order to avoid setting up the environment, manage packages and etc. You can create a free account here if you haven’t had an account yet.
Step 2. Create package
Since this is a simulation, let’s create a package called my_moving_cube_description under the simulation_ws.
cd ~/simulation_ws/src
catkin_create_pkg my_moving_cube_description rospy
We’ll start by building the URDF description of the robot. To do that, we’ll create a new folder called urdf under the my_moving_cube_description directory and create a file called my_moving_cube.urdf inside it with the following initial content. The robot tag indicates the name of the robot – my_moving_cube.
<robot name="my_moving_cube">
...
</robot>
Then let’s create the first link inside the robot. This includes 3 parts :
inertial: It defines the physical property of the link. You can calculate the inertia of an object by using this tool: rosrun spawn_robot_tools_pkg inertial_calculator.py
collision: It defines the collision property when the object interacts with other objects in the simulation.
visual: It defines the visual property, how the object will visually show in the simulation.
You also need to define the material property after the link if you want to use it in gazebo. (NOTICE: the reference of the material property should have the same name as the link)
Then we create a moving_cube.yaml file under the my_moving_cube_description/config to define parameters for the controller
# .yaml config file
#
# The PID gains and controller settings must be saved in a yaml file that gets loaded
# to the param server via the roslaunch file (moving_cube_control.launch).
my_moving_cube:
# Publish all joint states -----------------------------------
# Creates the /joint_states topic necessary in ROS
joint_state_controller:
type: joint_state_controller/JointStateController
publish_rate: 30
# Effort Controllers ---------------------------------------
inertia_wheel_roll_joint_velocity_controller:
type: effort_controllers/JointVelocityController
joint: inertia_wheel_roll_joint
pid: {p: 1.0, i: 0.0, d: 0.0}
In the end, you should create one new launch file called moving_cube control.launch under the launch folder to launch the controller
In this ROS LIVE-Class we’re going to create an OpenAI environment that can interact with your simulated robot in Gazebo, using ROS.
We will see:
How to define the actions required for the task
How to define the observation space
How to build the _step function that is executed at each training step.
Every Tuesday at 18:00 CET/CEST.
This is a LIVE Class on how to develop with ROS. In Live Classes you practice with me at the same time that I explain, with the provided free ROS material.
IMPORTANT: Remember to be on time for the class because at the beginning of the class we will share the code with the attendants.
IMPORTANT 2: in order to start practicing quickly, we are using the ROS Development Studio for doing the practice. You will need a free account to attend the class. Go to http://rds.theconstructsim.com and create an account prior to the class.
[irp posts=”9725″ name=”ROS Developers LIVE-Class #21: A Basic Example of OpenAI with ROS”]
In this ROS LIVE-Class we’re going to learn how to create our own Qlearning training for a cart pole in Gazebo using both OpenAI and ROS.
We will see:
How to create a Python training program that uses OpenAI infrastructure
How to create the environment that allows to get the observations and take the actions on a Gazebo simulation
How to interface everything with ROS
Every Tuesday at 18:00 CET/CEST.
This is a LIVE Class on how to develop with ROS. In Live Classes you practice with me at the same time that I explain, with the provided free ROS material.
IMPORTANT: Remember to be on time for the class because at the beginning of the class we will share the code with the attendants.
IMPORTANT 2: in order to start practicing quickly, we are using the ROS Development Studio for doing the practice. You will need a free account to attend the class. Go to ROS Development Stdio and create an account prior to the class.
[irp posts=”9720″ name=”ROS Developers LIVE-Class #22: How to create an OpenAI environment for your robotic problem”]
Got a suggestion for the next steps to take of this project? We would love to hear them in the comments bellow :).
Part 2
Step 1. Environment setup
We’ll start by explaining how to create the gym environment for training. You can create a file called monoped_env.py under the my_hopper_training/src directory with the following content
#!/usr/bin/env python
'''
By Miguel Angel Rodriguez <duckfrost@theconstructsim.com>
Visit our website at ec2-54-246-60-98.eu-west-1.compute.amazonaws.com
'''
import gym
import rospy
import numpy as np
import time
from gym import utils, spaces
from geometry_msgs.msg import Pose
from gym.utils import seeding
from gym.envs.registration import register
from gazebo_connection import GazeboConnection
from joint_publisher import JointPub
from monoped_state import MonopedState
from controllers_connection import ControllersConnection
#register the training environment in the gym as an available one
reg = register(
id='Monoped-v0',
entry_point='monoped_env:MonopedEnv',
timestep_limit=50,
)
class MonopedEnv(gym.Env):
def __init__(self):
# We assume that a ROS node has already been created
# before initialising the environment
# gets training parameters from param server
self.desired_pose = Pose()
self.desired_pose.position.x = rospy.get_param("/desired_pose/x")
self.desired_pose.position.y = rospy.get_param("/desired_pose/y")
self.desired_pose.position.z = rospy.get_param("/desired_pose/z")
self.running_step = rospy.get_param("/running_step")
self.max_incl = rospy.get_param("/max_incl")
self.max_height = rospy.get_param("/max_height")
self.min_height = rospy.get_param("/min_height")
self.joint_increment_value = rospy.get_param("/joint_increment_value")
self.done_reward = rospy.get_param("/done_reward")
self.alive_reward = rospy.get_param("/alive_reward")
self.desired_force = rospy.get_param("/desired_force")
self.desired_yaw = rospy.get_param("/desired_yaw")
self.weight_r1 = rospy.get_param("/weight_r1")
self.weight_r2 = rospy.get_param("/weight_r2")
self.weight_r3 = rospy.get_param("/weight_r3")
self.weight_r4 = rospy.get_param("/weight_r4")
self.weight_r5 = rospy.get_param("/weight_r5")
# stablishes connection with simulator
self.gazebo = GazeboConnection()
self.controllers_object = ControllersConnection(namespace="monoped")
self.monoped_state_object = MonopedState( max_height=self.max_height,
min_height=self.min_height,
abs_max_roll=self.max_incl,
abs_max_pitch=self.max_incl,
joint_increment_value=self.joint_increment_value,
done_reward=self.done_reward,
alive_reward=self.alive_reward,
desired_force=self.desired_force,
desired_yaw=self.desired_yaw,
weight_r1=self.weight_r1,
weight_r2=self.weight_r2,
weight_r3=self.weight_r3,
weight_r4=self.weight_r4,
weight_r5=self.weight_r5
)
self.monoped_state_object.set_desired_world_point(self.desired_pose.position.x,
self.desired_pose.position.y,
self.desired_pose.position.z)
self.monoped_joint_pubisher_object = JointPub()
"""
For this version, we consider 6 actions
1-2) Increment/Decrement haa_joint
3-4) Increment/Decrement hfe_joint
5-6) Increment/Decrement kfe_joint
"""
self.action_space = spaces.Discrete(6)
self.reward_range = (-np.inf, np.inf)
self._seed()
# A function to initialize the random generator
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
# Resets the state of the environment and returns an initial observation.
def _reset(self):
# 0st: We pause the Simulator
rospy.logdebug("Pausing SIM...")
self.gazebo.pauseSim()
# 1st: resets the simulation to initial values
rospy.logdebug("Reset SIM...")
self.gazebo.resetSim()
# 2nd: We Set the gravity to 0.0 so that we dont fall when reseting joints
# It also UNPAUSES the simulation
rospy.logdebug("Remove Gravity...")
self.gazebo.change_gravity(0.0, 0.0, 0.0)
# EXTRA: Reset JoinStateControlers because sim reset doesnt reset TFs, generating time problems
rospy.logdebug("reset_monoped_joint_controllers...")
self.controllers_object.reset_monoped_joint_controllers()
# 3rd: resets the robot to initial conditions
rospy.logdebug("set_init_pose...")
self.monoped_joint_pubisher_object.set_init_pose()
# 5th: Check all subscribers work.
# Get the state of the Robot defined by its RPY orientation, distance from
# desired point, contact force and JointState of the three joints
rospy.logdebug("check_all_systems_ready...")
self.monoped_state_object.check_all_systems_ready()
rospy.logdebug("get_observations...")
observation = self.monoped_state_object.get_observations()
# 6th: We restore the gravity to original
rospy.logdebug("Restore Gravity...")
self.gazebo.change_gravity(0.0, 0.0, -9.81)
# 7th: pauses simulation
rospy.logdebug("Pause SIM...")
self.gazebo.pauseSim()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state
def _step(self, action):
# Given the action selected by the learning algorithm,
# we perform the corresponding movement of the robot
# 1st, decide which action corresponsd to which joint is incremented
next_action_position = self.monoped_state_object.get_action_to_position(action)
# We move it to that pos
self.gazebo.unpauseSim()
self.monoped_joint_pubisher_object.move_joints(next_action_position)
# Then we send the command to the robot and let it go
# for running_step seconds
time.sleep(self.running_step)
self.gazebo.pauseSim()
# We now process the latest data saved in the class state to calculate
# the state and the rewards. This way we guarantee that they work
# with the same exact data.
# Generate State based on observations
observation = self.monoped_state_object.get_observations()
# finally we get an evaluation based on what happened in the sim
reward,done = self.monoped_state_object.process_data()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state, reward, done, {}
def get_state(self, observation):
"""
We retrieve the Stringuified-Discrete version of the given observation
:return: state
"""
return self.monoped_state_object.get_state_as_string(observation)
This script creates the gym environment with the following parts:
1.Register the training environment
You will always need to do this step to inform the gym package that a new training environment is created.
2.Load the desired pose
Parameters related to the training is loaded at this step.
3.Connect with the gazebo
In order to connect the gym environment with gazebo simulation, we create a script called gazebo_connection.py in the same directory with the following content
#!/usr/bin/env python
import rospy
from std_srvs.srv import Empty
from gazebo_msgs.msg import ODEPhysics
from gazebo_msgs.srv import SetPhysicsProperties, SetPhysicsPropertiesRequest
from std_msgs.msg import Float64
from geometry_msgs.msg import Vector3
class GazeboConnection():
def __init__(self):
self.unpause = rospy.ServiceProxy('/gazebo/unpause_physics', Empty)
self.pause = rospy.ServiceProxy('/gazebo/pause_physics', Empty)
self.reset_proxy = rospy.ServiceProxy('/gazebo/reset_simulation', Empty)
# Setup the Gravity Controle system
service_name = '/gazebo/set_physics_properties'
rospy.logdebug("Waiting for service " + str(service_name))
rospy.wait_for_service(service_name)
rospy.logdebug("Service Found " + str(service_name))
self.set_physics = rospy.ServiceProxy(service_name, SetPhysicsProperties)
self.init_values()
# We always pause the simulation, important for legged robots learning
self.pauseSim()
def pauseSim(self):
rospy.wait_for_service('/gazebo/pause_physics')
try:
self.pause()
except rospy.ServiceException, e:
print ("/gazebo/pause_physics service call failed")
def unpauseSim(self):
rospy.wait_for_service('/gazebo/unpause_physics')
try:
self.unpause()
except rospy.ServiceException, e:
print ("/gazebo/unpause_physics service call failed")
def resetSim(self):
rospy.wait_for_service('/gazebo/reset_simulation')
try:
self.reset_proxy()
except rospy.ServiceException, e:
print ("/gazebo/reset_simulation service call failed")
def resetWorld(self):
rospy.wait_for_service('/gazebo/reset_world')
try:
self.reset_proxy()
except rospy.ServiceException, e:
print ("/gazebo/reset_world service call failed")
def init_values(self):
rospy.wait_for_service('/gazebo/reset_simulation')
try:
# reset_proxy.call()
self.reset_proxy()
except rospy.ServiceException, e:
print ("/gazebo/reset_simulation service call failed")
self._time_step = Float64(0.001)
self._max_update_rate = Float64(1000.0)
self._gravity = Vector3()
self._gravity.x = 0.0
self._gravity.y = 0.0
self._gravity.z = 0.0
self._ode_config = ODEPhysics()
self._ode_config.auto_disable_bodies = False
self._ode_config.sor_pgs_precon_iters = 0
self._ode_config.sor_pgs_iters = 50
self._ode_config.sor_pgs_w = 1.3
self._ode_config.sor_pgs_rms_error_tol = 0.0
self._ode_config.contact_surface_layer = 0.001
self._ode_config.contact_max_correcting_vel = 0.0
self._ode_config.cfm = 0.0
self._ode_config.erp = 0.2
self._ode_config.max_contacts = 20
self.update_gravity_call()
def update_gravity_call(self):
self.pauseSim()
set_physics_request = SetPhysicsPropertiesRequest()
set_physics_request.time_step = self._time_step.data
set_physics_request.max_update_rate = self._max_update_rate.data
set_physics_request.gravity = self._gravity
set_physics_request.ode_config = self._ode_config
rospy.logdebug(str(set_physics_request.gravity))
result = self.set_physics(set_physics_request)
rospy.logdebug("Gravity Update Result==" + str(result.success) + ",message==" + str(result.status_message))
self.unpauseSim()
def change_gravity(self, x, y, z):
self._gravity.x = x
self._gravity.y = y
self._gravity.z = z
self.update_gravity_call()
This script is responsible to reset, pause and unpause the simulation for the training script.
4.Connect with the controller
Since TF doesn’t like that we reset the environment and it will generate some problems. We have to manually reset the controller with the controller_connection.py script. We won’t go into detail here. If you want to learn more about the controller, please check our controller 101 course.
#!/usr/bin/env python
'''
By Miguel Angel Rodriguez <duckfrost@theconstructsim.com>
Visit our website at ec2-54-246-60-98.eu-west-1.compute.amazonaws.com
'''
import gym
import rospy
import numpy as np
import time
from gym import utils, spaces
from geometry_msgs.msg import Pose
from gym.utils import seeding
from gym.envs.registration import register
from gazebo_connection import GazeboConnection
from joint_publisher import JointPub
from monoped_state import MonopedState
from controllers_connection import ControllersConnection
#register the training environment in the gym as an available one
reg = register(
id='Monoped-v0',
entry_point='monoped_env:MonopedEnv',
timestep_limit=50,
)
class MonopedEnv(gym.Env):
def __init__(self):
# We assume that a ROS node has already been created
# before initialising the environment
# gets training parameters from param server
self.desired_pose = Pose()
self.desired_pose.position.x = rospy.get_param("/desired_pose/x")
self.desired_pose.position.y = rospy.get_param("/desired_pose/y")
self.desired_pose.position.z = rospy.get_param("/desired_pose/z")
self.running_step = rospy.get_param("/running_step")
self.max_incl = rospy.get_param("/max_incl")
self.max_height = rospy.get_param("/max_height")
self.min_height = rospy.get_param("/min_height")
self.joint_increment_value = rospy.get_param("/joint_increment_value")
self.done_reward = rospy.get_param("/done_reward")
self.alive_reward = rospy.get_param("/alive_reward")
self.desired_force = rospy.get_param("/desired_force")
self.desired_yaw = rospy.get_param("/desired_yaw")
self.weight_r1 = rospy.get_param("/weight_r1")
self.weight_r2 = rospy.get_param("/weight_r2")
self.weight_r3 = rospy.get_param("/weight_r3")
self.weight_r4 = rospy.get_param("/weight_r4")
self.weight_r5 = rospy.get_param("/weight_r5")
# stablishes connection with simulator
self.gazebo = GazeboConnection()
self.controllers_object = ControllersConnection(namespace="monoped")
self.monoped_state_object = MonopedState( max_height=self.max_height,
min_height=self.min_height,
abs_max_roll=self.max_incl,
abs_max_pitch=self.max_incl,
joint_increment_value=self.joint_increment_value,
done_reward=self.done_reward,
alive_reward=self.alive_reward,
desired_force=self.desired_force,
desired_yaw=self.desired_yaw,
weight_r1=self.weight_r1,
weight_r2=self.weight_r2,
weight_r3=self.weight_r3,
weight_r4=self.weight_r4,
weight_r5=self.weight_r5
)
self.monoped_state_object.set_desired_world_point(self.desired_pose.position.x,
self.desired_pose.position.y,
self.desired_pose.position.z)
self.monoped_joint_pubisher_object = JointPub()
"""
For this version, we consider 6 actions
1-2) Increment/Decrement haa_joint
3-4) Increment/Decrement hfe_joint
5-6) Increment/Decrement kfe_joint
"""
self.action_space = spaces.Discrete(6)
self.reward_range = (-np.inf, np.inf)
self._seed()
# A function to initialize the random generator
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
# Resets the state of the environment and returns an initial observation.
def _reset(self):
# 0st: We pause the Simulator
rospy.logdebug("Pausing SIM...")
self.gazebo.pauseSim()
# 1st: resets the simulation to initial values
rospy.logdebug("Reset SIM...")
self.gazebo.resetSim()
# 2nd: We Set the gravity to 0.0 so that we dont fall when reseting joints
# It also UNPAUSES the simulation
rospy.logdebug("Remove Gravity...")
self.gazebo.change_gravity(0.0, 0.0, 0.0)
# EXTRA: Reset JoinStateControlers because sim reset doesnt reset TFs, generating time problems
rospy.logdebug("reset_monoped_joint_controllers...")
self.controllers_object.reset_monoped_joint_controllers()
# 3rd: resets the robot to initial conditions
rospy.logdebug("set_init_pose...")
self.monoped_joint_pubisher_object.set_init_pose()
# 5th: Check all subscribers work.
# Get the state of the Robot defined by its RPY orientation, distance from
# desired point, contact force and JointState of the three joints
rospy.logdebug("check_all_systems_ready...")
self.monoped_state_object.check_all_systems_ready()
rospy.logdebug("get_observations...")
observation = self.monoped_state_object.get_observations()
# 6th: We restore the gravity to original
rospy.logdebug("Restore Gravity...")
self.gazebo.change_gravity(0.0, 0.0, -9.81)
# 7th: pauses simulation
rospy.logdebug("Pause SIM...")
self.gazebo.pauseSim()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state
def _step(self, action):
# Given the action selected by the learning algorithm,
# we perform the corresponding movement of the robot
# 1st, decide which action corresponsd to which joint is incremented
next_action_position = self.monoped_state_object.get_action_to_position(action)
# We move it to that pos
self.gazebo.unpauseSim()
self.monoped_joint_pubisher_object.move_joints(next_action_position)
# Then we send the command to the robot and let it go
# for running_step seconds
time.sleep(self.running_step)
self.gazebo.pauseSim()
# We now process the latest data saved in the class state to calculate
# the state and the rewards. This way we guarantee that they work
# with the same exact data.
# Generate State based on observations
observation = self.monoped_state_object.get_observations()
# finally we get an evaluation based on what happened in the sim
reward,done = self.monoped_state_object.process_data()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state, reward, done, {}
def get_state(self, observation):
"""
We retrieve the Stringuified-Discrete version of the given observation
:return: state
"""
return self.monoped_state_object.get_state_as_string(observation)
5.Generate state object
The state object is then generated with the current state with the following monoped_state.py script
#!/usr/bin/env python
'''
By Miguel Angel Rodriguez <duckfrost@theconstructsim.com>
Visit our website at ec2-54-246-60-98.eu-west-1.compute.amazonaws.com
'''
import gym
import rospy
import numpy as np
import time
from gym import utils, spaces
from geometry_msgs.msg import Pose
from gym.utils import seeding
from gym.envs.registration import register
from gazebo_connection import GazeboConnection
from joint_publisher import JointPub
from monoped_state import MonopedState
from controllers_connection import ControllersConnection
#register the training environment in the gym as an available one
reg = register(
id='Monoped-v0',
entry_point='monoped_env:MonopedEnv',
timestep_limit=50,
)
class MonopedEnv(gym.Env):
def __init__(self):
# We assume that a ROS node has already been created
# before initialising the environment
# gets training parameters from param server
self.desired_pose = Pose()
self.desired_pose.position.x = rospy.get_param("/desired_pose/x")
self.desired_pose.position.y = rospy.get_param("/desired_pose/y")
self.desired_pose.position.z = rospy.get_param("/desired_pose/z")
self.running_step = rospy.get_param("/running_step")
self.max_incl = rospy.get_param("/max_incl")
self.max_height = rospy.get_param("/max_height")
self.min_height = rospy.get_param("/min_height")
self.joint_increment_value = rospy.get_param("/joint_increment_value")
self.done_reward = rospy.get_param("/done_reward")
self.alive_reward = rospy.get_param("/alive_reward")
self.desired_force = rospy.get_param("/desired_force")
self.desired_yaw = rospy.get_param("/desired_yaw")
self.weight_r1 = rospy.get_param("/weight_r1")
self.weight_r2 = rospy.get_param("/weight_r2")
self.weight_r3 = rospy.get_param("/weight_r3")
self.weight_r4 = rospy.get_param("/weight_r4")
self.weight_r5 = rospy.get_param("/weight_r5")
# stablishes connection with simulator
self.gazebo = GazeboConnection()
self.controllers_object = ControllersConnection(namespace="monoped")
self.monoped_state_object = MonopedState( max_height=self.max_height,
min_height=self.min_height,
abs_max_roll=self.max_incl,
abs_max_pitch=self.max_incl,
joint_increment_value=self.joint_increment_value,
done_reward=self.done_reward,
alive_reward=self.alive_reward,
desired_force=self.desired_force,
desired_yaw=self.desired_yaw,
weight_r1=self.weight_r1,
weight_r2=self.weight_r2,
weight_r3=self.weight_r3,
weight_r4=self.weight_r4,
weight_r5=self.weight_r5
)
self.monoped_state_object.set_desired_world_point(self.desired_pose.position.x,
self.desired_pose.position.y,
self.desired_pose.position.z)
self.monoped_joint_pubisher_object = JointPub()
"""
For this version, we consider 6 actions
1-2) Increment/Decrement haa_joint
3-4) Increment/Decrement hfe_joint
5-6) Increment/Decrement kfe_joint
"""
self.action_space = spaces.Discrete(6)
self.reward_range = (-np.inf, np.inf)
self._seed()
# A function to initialize the random generator
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
# Resets the state of the environment and returns an initial observation.
def _reset(self):
# 0st: We pause the Simulator
rospy.logdebug("Pausing SIM...")
self.gazebo.pauseSim()
# 1st: resets the simulation to initial values
rospy.logdebug("Reset SIM...")
self.gazebo.resetSim()
# 2nd: We Set the gravity to 0.0 so that we dont fall when reseting joints
# It also UNPAUSES the simulation
rospy.logdebug("Remove Gravity...")
self.gazebo.change_gravity(0.0, 0.0, 0.0)
# EXTRA: Reset JoinStateControlers because sim reset doesnt reset TFs, generating time problems
rospy.logdebug("reset_monoped_joint_controllers...")
self.controllers_object.reset_monoped_joint_controllers()
# 3rd: resets the robot to initial conditions
rospy.logdebug("set_init_pose...")
self.monoped_joint_pubisher_object.set_init_pose()
# 5th: Check all subscribers work.
# Get the state of the Robot defined by its RPY orientation, distance from
# desired point, contact force and JointState of the three joints
rospy.logdebug("check_all_systems_ready...")
self.monoped_state_object.check_all_systems_ready()
rospy.logdebug("get_observations...")
observation = self.monoped_state_object.get_observations()
# 6th: We restore the gravity to original
rospy.logdebug("Restore Gravity...")
self.gazebo.change_gravity(0.0, 0.0, -9.81)
# 7th: pauses simulation
rospy.logdebug("Pause SIM...")
self.gazebo.pauseSim()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state
def _step(self, action):
# Given the action selected by the learning algorithm,
# we perform the corresponding movement of the robot
# 1st, decide which action corresponsd to which joint is incremented
next_action_position = self.monoped_state_object.get_action_to_position(action)
# We move it to that pos
self.gazebo.unpauseSim()
self.monoped_joint_pubisher_object.move_joints(next_action_position)
# Then we send the command to the robot and let it go
# for running_step seconds
time.sleep(self.running_step)
self.gazebo.pauseSim()
# We now process the latest data saved in the class state to calculate
# the state and the rewards. This way we guarantee that they work
# with the same exact data.
# Generate State based on observations
observation = self.monoped_state_object.get_observations()
# finally we get an evaluation based on what happened in the sim
reward,done = self.monoped_state_object.process_data()
# Get the State Discrete Stringuified version of the observations
state = self.get_state(observation)
return state, reward, done, {}
def get_state(self, observation):
"""
We retrieve the Stringuified-Discrete version of the given observation
:return: state
"""
return self.monoped_state_object.get_state_as_string(observation)
6.Reset
Setup the environment back to its initial observation. This includes the steps like pause the simulation, reset the controller and etc. as we described before.
7.Step
In this part, an action will be decided based on the current state and then be published with the following joint_publisher.py script. After that, the reward is calculated based on the new observation.
#!/usr/bin/env python
import rospy
import math
from std_msgs.msg import String
from std_msgs.msg import Float64
class JointPub(object):
def __init__(self):
self.publishers_array = []
self._haa_joint_pub = rospy.Publisher('/monoped/haa_joint_position_controller/command', Float64, queue_size=1)
self._hfe_joint_pub = rospy.Publisher('/monoped/hfe_joint_position_controller/command', Float64, queue_size=1)
self._kfe_joint_pub = rospy.Publisher('/monoped/kfe_joint_position_controller/command', Float64, queue_size=1)
self.publishers_array.append(self._haa_joint_pub)
self.publishers_array.append(self._hfe_joint_pub)
self.publishers_array.append(self._kfe_joint_pub)
self.init_pos = [0.0,0.0,0.0]
def set_init_pose(self):
"""
Sets joints to initial position [0,0,0]
:return:
"""
self.check_publishers_connection()
self.move_joints(self.init_pos)
def check_publishers_connection(self):
"""
Checks that all the publishers are working
:return:
"""
rate = rospy.Rate(10) # 10hz
while (self._haa_joint_pub.get_num_connections() == 0):
rospy.logdebug("No susbribers to _haa_joint_pub yet so we wait and try again")
try:
rate.sleep()
except rospy.ROSInterruptException:
# This is to avoid error when world is rested, time when backwards.
pass
rospy.logdebug("_haa_joint_pub Publisher Connected")
while (self._hfe_joint_pub.get_num_connections() == 0):
rospy.logdebug("No susbribers to _hfe_joint_pub yet so we wait and try again")
try:
rate.sleep()
except rospy.ROSInterruptException:
# This is to avoid error when world is rested, time when backwards.
pass
rospy.logdebug("_hfe_joint_pub Publisher Connected")
while (self._kfe_joint_pub.get_num_connections() == 0):
rospy.logdebug("No susbribers to _kfe_joint_pub yet so we wait and try again")
try:
rate.sleep()
except rospy.ROSInterruptException:
# This is to avoid error when world is rested, time when backwards.
pass
rospy.logdebug("_kfe_joint_pub Publisher Connected")
rospy.logdebug("All Publishers READY")
def joint_mono_des_callback(self, msg):
rospy.logdebug(str(msg.joint_state.position))
self.move_joints(msg.joint_state.position)
def move_joints(self, joints_array):
i = 0
for publisher_object in self.publishers_array:
joint_value = Float64()
joint_value.data = joints_array[i]
rospy.logdebug("JointsPos>>"+str(joint_value))
publisher_object.publish(joint_value)
i += 1
def start_loop(self, rate_value = 2.0):
rospy.logdebug("Start Loop")
pos1 = [0.0,0.0,1.6]
pos2 = [0.0,0.0,-1.6]
position = "pos1"
rate = rospy.Rate(rate_value)
while not rospy.is_shutdown():
if position == "pos1":
self.move_joints(pos1)
position = "pos2"
else:
self.move_joints(pos2)
position = "pos1"
rate.sleep()
def start_sinus_loop(self, rate_value = 2.0):
rospy.logdebug("Start Loop")
w = 0.0
x = 2.0*math.sin(w)
#pos_x = [0.0,0.0,x]
#pos_x = [x, 0.0, 0.0]
pos_x = [0.0, x, 0.0]
rate = rospy.Rate(rate_value)
while not rospy.is_shutdown():
self.move_joints(pos_x)
w += 0.05
x = 2.0 * math.sin(w)
#pos_x = [0.0, 0.0, x]
#pos_x = [x, 0.0, 0.0]
pos_x = [0.0, x, 0.0]
rate.sleep()
if __name__=="__main__":
rospy.init_node('joint_publisher_node')
joint_publisher = JointPub()
rate_value = 50.0
#joint_publisher.start_loop(rate_value)
joint_publisher.start_sinus_loop(rate_value)
Step 2. Training
Now you have all the code you need to start training. Let’s run the simulation first. Go to Simulations -> Select launch file -> my_legged_robot_sims->main.launch to launch the hopper robot simulation in Gazebo.
Then you can run the training with
roslaunch my_hopper_training main.launch
The algorithm is working and start to train the robot to perform the task we want, however, it still requires lots of tuning to work properly.
You can have multiple instances and train robots with different parameters in parallel with our paid program in ROSDS. Please check it here if you are interested.
![[ROS Projects] OpenAI with Moving Cube Robot in Gazebo Step-by-Step Part2](https://www.theconstruct.ai/wp-content/uploads/2018/06/OpenAI-with-Moving-Cube-Robot-in-Gazebo-Step-by-Step-Part2.png)
![[ROS Projects] OpenAI with Moving Cube Robot in Gazebo Step-by-Step Part1](https://www.theconstruct.ai/wp-content/uploads/2018/06/ROS-Projects-OpenAI-with-Moving-Cube-Robot-in-Gazebo-Step-by-Step.png)
![[ROS Projects] OpenAI with Hopper Robot in Gazebo Step-by-Step #Part2](https://www.theconstruct.ai/wp-content/uploads/2018/06/OpenAI-with-Hopper-Robot-in-Gazebo-Step-by-Step-Part2.png)
