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update minitaur gym env

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This commit is contained in:
Erwin Coumans
2017-08-23 17:41:34 -07:00
parent a9fb0033a4
commit 0851b45f39
7 changed files with 986 additions and 501 deletions
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29
examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py Normal file
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import functools
import inspect
import pybullet
class BulletClient(object):
"""A wrapper for pybullet to manage different clients."""
def __init__(self, connection_mode=pybullet.DIRECT):
"""Create a simulation and connect to it."""
self._client = pybullet.connect(connection_mode)
self._shapes = {}
def __del__(self):
"""Clean up connection if not already done."""
try:
pybullet.disconnect(physicsClientId=self._client)
except pybullet.error:
pass
def __getattr__(self, name):
"""Inject the client id into Bullet functions."""
attribute = getattr(pybullet, name)
if inspect.isbuiltin(attribute):
if name not in ["invertTransform", "multiplyTransforms",
"getMatrixFromQuaternion"]: # A temporary hack for now.
attribute = functools.partial(attribute, physicsClientId=self._client)
return attribute

632
examples/pybullet/gym/pybullet_envs/bullet/minitaur.py
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import pybullet as p """This file implements the functionalities of a minitaur using pybullet.
"""
import copy
import math
import numpy as np import numpy as np
import motor
import os
class Minitaur: INIT_POSITION = [0, 0, .2]
def __init__(self, urdfRootPath=''): INIT_ORIENTATION = [0, 0, 0, 1]
self.urdfRootPath = urdfRootPath KNEE_CONSTRAINT_POINT_RIGHT = [0, 0.005, 0.2]
self.reset() KNEE_CONSTRAINT_POINT_LEFT = [0, 0.01, 0.2]
OVERHEAT_SHUTDOWN_TORQUE = 2.45
def applyAction(self, motorCommands): OVERHEAT_SHUTDOWN_TIME = 1.0
motorCommandsWithDir = np.multiply(motorCommands, self.motorDir) LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"]
for i in range(self.nMotors): MOTOR_NAMES = [
self.setMotorAngleById(self.motorIdList[i], motorCommandsWithDir[i]) "motor_front_leftL_joint", "motor_front_leftR_joint",
"motor_back_leftL_joint", "motor_back_leftR_joint",
def getObservation(self): "motor_front_rightL_joint", "motor_front_rightR_joint",
observation = [] "motor_back_rightL_joint", "motor_back_rightR_joint"
observation.extend(self.getMotorAngles().tolist()) ]
observation.extend(self.getMotorVelocities().tolist()) LEG_LINK_ID = [2, 3, 5, 6, 8, 9, 11, 12, 15, 16, 18, 19, 21, 22, 24, 25]
observation.extend(self.getMotorTorques().tolist()) MOTOR_LINK_ID = [1, 4, 7, 10, 14, 17, 20, 23]
observation.extend(list(self.getBaseOrientation())) FOOT_LINK_ID = [3, 6, 9, 12, 16, 19, 22, 25]
observation.extend(list(self.getBasePosition())) BASE_LINK_ID = -1
return observation
def getActionDimension(self):
return self.nMotors
def getObservationDimension(self):
return len(self.getObservation())
def buildJointNameToIdDict(self):
nJoints = p.getNumJoints(self.quadruped)
self.jointNameToId = {}
for i in range(nJoints):
jointInfo = p.getJointInfo(self.quadruped, i)
self.jointNameToId[jointInfo[1].decode('UTF-8')] = jointInfo[0]
self.resetPose()
for i in range(100):
p.stepSimulation()
def buildMotorIdList(self):
self.motorIdList.append(self.jointNameToId['motor_front_leftR_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_leftL_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_leftR_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_leftL_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_rightL_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_rightR_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_rightL_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_rightR_joint'])
def reset(self): class Minitaur(object):
self.quadruped = p.loadURDF("%s/quadruped/quadruped.urdf" % self.urdfRootPath,0,0,.3) """The minitaur class that simulates a quadruped robot from Ghost Robotics.
self.kp = 1
self.kd = 0.1
self.maxForce = 3.5
self.nMotors = 8
self.motorIdList = []
self.motorDir = [1, -1, 1, -1, -1, 1, -1, 1]
self.buildJointNameToIdDict()
self.buildMotorIdList()
"""
def disableAllMotors(self): def __init__(self,
nJoints = p.getNumJoints(self.quadruped) pybullet_client,
for i in range(nJoints): urdf_root= os.path.join(os.path.dirname(__file__),"../data"),
p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=i, controlMode=p.VELOCITY_CONTROL, force=0) time_step=0.01,
self_collision_enabled=False,
motor_velocity_limit=np.inf,
pd_control_enabled=False,
accurate_motor_model_enabled=False,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=False,
on_rack=False,
kd_for_pd_controllers=0.3):
"""Constructs a minitaur and reset it to the initial states.
def setMotorAngleById(self, motorId, desiredAngle): Args:
p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=motorId, controlMode=p.POSITION_CONTROL, targetPosition=desiredAngle, positionGain=self.kp, velocityGain=self.kd, force=self.maxForce) pybullet_client: The instance of BulletClient to manage different
simulations.
urdf_root: The path to the urdf folder.
time_step: The time step of the simulation.
self_collision_enabled: Whether to enable self collision.
motor_velocity_limit: The upper limit of the motor velocity.
pd_control_enabled: Whether to use PD control for the motors.
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 acurate 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.
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.
kd_for_pd_controllers: kd value for the pd controllers of the motors.
"""
self.num_motors = 8
self.num_legs = self.num_motors / 2
self._pybullet_client = pybullet_client
self._urdf_root = urdf_root
self._self_collision_enabled = self_collision_enabled
self._motor_velocity_limit = motor_velocity_limit
self._pd_control_enabled = pd_control_enabled
self._motor_direction = [-1, -1, -1, -1, 1, 1, 1, 1]
self._observed_motor_torques = np.zeros(self.num_motors)
self._applied_motor_torques = np.zeros(self.num_motors)
self._max_force = 3.5
self._accurate_motor_model_enabled = accurate_motor_model_enabled
self._torque_control_enabled = torque_control_enabled
self._motor_overheat_protection = motor_overheat_protection
self._on_rack = on_rack
if self._accurate_motor_model_enabled:
self._kp = motor_kp
self._kd = motor_kd
self._motor_model = motor.MotorModel(
torque_control_enabled=self._torque_control_enabled,
kp=self._kp,
kd=self._kd)
elif self._pd_control_enabled:
self._kp = 8
self._kd = kd_for_pd_controllers
else:
self._kp = 1
self._kd = 1
self.time_step = time_step
self.Reset()
def setMotorAngleByName(self, motorName, desiredAngle): def _RecordMassInfoFromURDF(self):
self.setMotorAngleById(self.jointNameToId[motorName], desiredAngle) self._base_mass_urdf = self._pybullet_client.getDynamicsInfo(
self.quadruped, BASE_LINK_ID)[0]
self._leg_masses_urdf = []
self._leg_masses_urdf.append(
self._pybullet_client.getDynamicsInfo(self.quadruped, LEG_LINK_ID[0])[
0])
self._leg_masses_urdf.append(
self._pybullet_client.getDynamicsInfo(self.quadruped, MOTOR_LINK_ID[0])[
0])
def _BuildJointNameToIdDict(self):
num_joints = self._pybullet_client.getNumJoints(self.quadruped)
self._joint_name_to_id = {}
for i in xrange(num_joints):
joint_info = self._pybullet_client.getJointInfo(self.quadruped, i)
self._joint_name_to_id[joint_info[1].decode("UTF-8")] = joint_info[0]
def resetPose(self): def _BuildMotorIdList(self):
#right front leg self._motor_id_list = [
self.disableAllMotors() self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightR_joint'],1.57) ]
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightR_link'],-2.2)
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightL_joint'],-1.57)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightL_link'],2.2)
p.createConstraint(self.quadruped,self.jointNameToId['knee_front_rightR_link'],self.quadruped,self.jointNameToId['knee_front_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.01,0.2],[0,-0.015,0.2])
self.setMotorAngleByName('motor_front_rightR_joint', 1.57)
self.setMotorAngleByName('motor_front_rightL_joint',-1.57)
#left front leg def Reset(self, reload_urdf=True):
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftR_joint'],1.57) """Reset the minitaur to its initial states.
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftR_link'],-2.2)
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftL_joint'],-1.57)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftL_link'],2.2)
p.createConstraint(self.quadruped,self.jointNameToId['knee_front_leftR_link'],self.quadruped,self.jointNameToId['knee_front_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,-0.01,0.2],[0,0.015,0.2])
self.setMotorAngleByName('motor_front_leftR_joint', 1.57)
self.setMotorAngleByName('motor_front_leftL_joint',-1.57)
#right back leg Args:
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightR_joint'],1.57) reload_urdf: Whether to reload the urdf file. If not, Reset() just place
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightR_link'],-2.2) the minitaur back to its starting position.
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightL_joint'],-1.57) """
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightL_link'],2.2) if reload_urdf:
p.createConstraint(self.quadruped,self.jointNameToId['knee_back_rightR_link'],self.quadruped,self.jointNameToId['knee_back_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.01,0.2],[0,-0.015,0.2]) if self._self_collision_enabled:
self.setMotorAngleByName('motor_back_rightR_joint', 1.57) self.quadruped = self._pybullet_client.loadURDF(
self.setMotorAngleByName('motor_back_rightL_joint',-1.57) "%s/quadruped/minitaur.urdf" % self._urdf_root,
INIT_POSITION,
flags=self._pybullet_client.URDF_USE_SELF_COLLISION)
else:
self.quadruped = self._pybullet_client.loadURDF(
"%s/quadruped/minitaur.urdf" % self._urdf_root, INIT_POSITION)
self._BuildJointNameToIdDict()
self._BuildMotorIdList()
self._RecordMassInfoFromURDF()
self.ResetPose(add_constraint=True)
if self._on_rack:
self._pybullet_client.createConstraint(
self.quadruped, -1, -1, -1, self._pybullet_client.JOINT_FIXED,
[0, 0, 0], [0, 0, 0], [0, 0, 1])
else:
self._pybullet_client.resetBasePositionAndOrientation(
self.quadruped, INIT_POSITION, INIT_ORIENTATION)
self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0],
[0, 0, 0])
self.ResetPose(add_constraint=False)
#left back leg self._overheat_counter = np.zeros(self.num_motors)
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftR_joint'],1.57) self._motor_enabled_list = [True] * self.num_motors
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftR_link'],-2.2)
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftL_joint'],-1.57)
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftL_link'],2.2)
p.createConstraint(self.quadruped,self.jointNameToId['knee_back_leftR_link'],self.quadruped,self.jointNameToId['knee_back_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,-0.01,0.2],[0,0.015,0.2])
self.setMotorAngleByName('motor_back_leftR_joint', 1.57)
self.setMotorAngleByName('motor_back_leftL_joint',-1.57)
def getBasePosition(self): def _SetMotorTorqueById(self, motor_id, torque):
position, orientation = p.getBasePositionAndOrientation(self.quadruped) self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=motor_id,
controlMode=self._pybullet_client.TORQUE_CONTROL,
force=torque)
def _SetDesiredMotorAngleById(self, motor_id, desired_angle):
self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=motor_id,
controlMode=self._pybullet_client.POSITION_CONTROL,
targetPosition=desired_angle,
positionGain=self._kp,
velocityGain=self._kd,
force=self._max_force)
def _SetDesiredMotorAngleByName(self, motor_name, desired_angle):
self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name],
desired_angle)
def ResetPose(self, add_constraint):
"""Reset the pose of the minitaur.
Args:
add_constraint: Whether to add a constraint at the joints of two feet.
"""
for i in xrange(self.num_legs):
self._ResetPoseForLeg(i, add_constraint)
def _ResetPoseForLeg(self, leg_id, add_constraint):
"""Reset the initial pose for the leg.
Args:
leg_id: It should be 0, 1, 2, or 3, which represents the leg at
front_left, back_left, front_right and back_right.
add_constraint: Whether to add a constraint at the joints of two feet.
"""
knee_friction_force = 0
half_pi = math.pi / 2.0
knee_angle = -2.1834
leg_position = LEG_POSITION[leg_id]
self._pybullet_client.resetJointState(
self.quadruped,
self._joint_name_to_id["motor_" + leg_position + "L_joint"],
self._motor_direction[2 * leg_id] * half_pi,
targetVelocity=0)
self._pybullet_client.resetJointState(
self.quadruped,
self._joint_name_to_id["knee_" + leg_position + "L_link"],
self._motor_direction[2 * leg_id] * knee_angle,
targetVelocity=0)
self._pybullet_client.resetJointState(
self.quadruped,
self._joint_name_to_id["motor_" + leg_position + "R_joint"],
self._motor_direction[2 * leg_id + 1] * half_pi,
targetVelocity=0)
self._pybullet_client.resetJointState(
self.quadruped,
self._joint_name_to_id["knee_" + leg_position + "R_link"],
self._motor_direction[2 * leg_id + 1] * knee_angle,
targetVelocity=0)
if add_constraint:
self._pybullet_client.createConstraint(
self.quadruped, self._joint_name_to_id["knee_"
+ leg_position + "R_link"],
self.quadruped, self._joint_name_to_id["knee_"
+ leg_position + "L_link"],
self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT)
if self._accurate_motor_model_enabled or self._pd_control_enabled:
# Disable the default motor in pybullet.
self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=(self._joint_name_to_id["motor_"
+ leg_position + "L_joint"]),
controlMode=self._pybullet_client.VELOCITY_CONTROL,
targetVelocity=0,
force=knee_friction_force)
self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=(self._joint_name_to_id["motor_"
+ leg_position + "R_joint"]),
controlMode=self._pybullet_client.VELOCITY_CONTROL,
targetVelocity=0,
force=knee_friction_force)
else:
self._SetDesiredMotorAngleByName(
"motor_" + leg_position + "L_joint",
self._motor_direction[2 * leg_id] * half_pi)
self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint",
self._motor_direction[2 * leg_id
+ 1] * half_pi)
self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=(self._joint_name_to_id["knee_" + leg_position + "L_link"]),
controlMode=self._pybullet_client.VELOCITY_CONTROL,
targetVelocity=0,
force=knee_friction_force)
self._pybullet_client.setJointMotorControl2(
bodyIndex=self.quadruped,
jointIndex=(self._joint_name_to_id["knee_" + leg_position + "R_link"]),
controlMode=self._pybullet_client.VELOCITY_CONTROL,
targetVelocity=0,
force=knee_friction_force)
def GetBasePosition(self):
"""Get the position of minitaur's base.
Returns:
The position of minitaur's base.
"""
position, _ = (
self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
return position return position
def getBaseOrientation(self): def GetBaseOrientation(self):
position, orientation = p.getBasePositionAndOrientation(self.quadruped) """Get the orientation of minitaur's base, represented as quaternion.
Returns:
The orientation of minitaur's base.
"""
_, orientation = (
self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
return orientation return orientation
def getMotorAngles(self): def GetActionDimension(self):
motorAngles = [] """Get the length of the action list.
for i in range(self.nMotors):
jointState = p.getJointState(self.quadruped, self.motorIdList[i])
motorAngles.append(jointState[0])
motorAngles = np.multiply(motorAngles, self.motorDir)
return motorAngles
def getMotorVelocities(self): Returns:
motorVelocities = [] The length of the action list.
for i in range(self.nMotors): """
jointState = p.getJointState(self.quadruped, self.motorIdList[i]) return self.num_motors
motorVelocities.append(jointState[1])
motorVelocities = np.multiply(motorVelocities, self.motorDir)
return motorVelocities
def getMotorTorques(self): def GetObservationUpperBound(self):
motorTorques = [] """Get the upper bound of the observation.
for i in range(self.nMotors):
jointState = p.getJointState(self.quadruped, self.motorIdList[i]) Returns:
motorTorques.append(jointState[3]) The upper bound of an observation. See GetObservation() for the details
motorTorques = np.multiply(motorTorques, self.motorDir) of each element of an observation.
return motorTorques """
upper_bound = np.array([0.0] * self.GetObservationDimension())
upper_bound[0:self.num_motors] = math.pi # Joint angle.
upper_bound[self.num_motors:2 * self.num_motors] = (
motor.MOTOR_SPEED_LIMIT) # Joint velocity.
upper_bound[2 * self.num_motors:3 * self.num_motors] = (
motor.OBSERVED_TORQUE_LIMIT) # Joint torque.
upper_bound[3 * self.num_motors:] = 1.0 # Quaternion of base orientation.
return upper_bound
def GetObservationLowerBound(self):
"""Get the lower bound of the observation."""
return -self.GetObservationUpperBound()
def GetObservationDimension(self):
"""Get the length of the observation list.
Returns:
The length of the observation list.
"""
return len(self.GetObservation())
def GetObservation(self):
"""Get the observations of minitaur.
It includes the angles, velocities, torques and the orientation of the base.
Returns:
The observation list. observation[0:8] are motor angles. observation[8:16]
are motor velocities, observation[16:24] are motor torques.
observation[24:28] is the orientation of the base, in quaternion form.
"""
observation = []
observation.extend(self.GetMotorAngles().tolist())
observation.extend(self.GetMotorVelocities().tolist())
observation.extend(self.GetMotorTorques().tolist())
observation.extend(list(self.GetBaseOrientation()))
return observation
def ApplyAction(self, motor_commands):
"""Set the desired motor angles to the motors of the minitaur.
The desired motor angles are clipped based on the maximum allowed velocity.
If the pd_control_enabled is True, a torque is calculated according to
the difference between current and desired joint angle, as well as the joint
velocity. This torque is exerted to the motor. For more information about
PD control, please refer to: https://en.wikipedia.org/wiki/PID_controller.
Args:
motor_commands: The eight desired motor angles.
"""
if self._motor_velocity_limit < np.inf:
current_motor_angle = self.GetMotorAngles()
motor_commands_max = (
current_motor_angle + self.time_step * self._motor_velocity_limit)
motor_commands_min = (
current_motor_angle - self.time_step * self._motor_velocity_limit)
motor_commands = np.clip(motor_commands, motor_commands_min,
motor_commands_max)
if self._accurate_motor_model_enabled or self._pd_control_enabled:
q = self.GetMotorAngles()
qdot = self.GetMotorVelocities()
if self._accurate_motor_model_enabled:
actual_torque, observed_torque = self._motor_model.convert_to_torque(
motor_commands, q, qdot)
if self._motor_overheat_protection:
for i in xrange(self.num_motors):
if abs(actual_torque[i]) > OVERHEAT_SHUTDOWN_TORQUE:
self._overheat_counter[i] += 1
else:
self._overheat_counter[i] = 0
if (self._overheat_counter[i] >
OVERHEAT_SHUTDOWN_TIME / self.time_step):
self._motor_enabled_list[i] = False
# The torque is already in the observation space because we use
# GetMotorAngles and GetMotorVelocities.
self._observed_motor_torques = observed_torque
# Transform into the motor space when applying the torque.
self._applied_motor_torque = np.multiply(actual_torque,
self._motor_direction)
for motor_id, motor_torque, motor_enabled in zip(
self._motor_id_list, self._applied_motor_torque,
self._motor_enabled_list):
if motor_enabled:
self._SetMotorTorqueById(motor_id, motor_torque)
else:
self._SetMotorTorqueById(motor_id, 0)
else:
torque_commands = -self._kp * (q - motor_commands) - self._kd * qdot
# The torque is already in the observation space because we use
# GetMotorAngles and GetMotorVelocities.
self._observed_motor_torques = torque_commands
# Transform into the motor space when applying the torque.
self._applied_motor_torques = np.multiply(self._observed_motor_torques,
self._motor_direction)
for motor_id, motor_torque in zip(self._motor_id_list,
self._applied_motor_torques):
self._SetMotorTorqueById(motor_id, motor_torque)
else:
motor_commands_with_direction = np.multiply(motor_commands,
self._motor_direction)
for motor_id, motor_command_with_direction in zip(
self._motor_id_list, motor_commands_with_direction):
self._SetDesiredMotorAngleById(motor_id, motor_command_with_direction)
def GetMotorAngles(self):
"""Get the eight motor angles at the current moment.
Returns:
Motor angles.
"""
motor_angles = [
self._pybullet_client.getJointState(self.quadruped, motor_id)[0]
for motor_id in self._motor_id_list
]
motor_angles = np.multiply(motor_angles, self._motor_direction)
return motor_angles
def GetMotorVelocities(self):
"""Get the velocity of all eight motors.
Returns:
Velocities of all eight motors.
"""
motor_velocities = [
self._pybullet_client.getJointState(self.quadruped, motor_id)[1]
for motor_id in self._motor_id_list
]
motor_velocities = np.multiply(motor_velocities, self._motor_direction)
return motor_velocities
def GetMotorTorques(self):
"""Get the amount of torques the motors are exerting.
Returns:
Motor torques of all eight motors.
"""
if self._accurate_motor_model_enabled or self._pd_control_enabled:
return self._observed_motor_torques
else:
motor_torques = [
self._pybullet_client.getJointState(self.quadruped, motor_id)[3]
for motor_id in self._motor_id_list
]
motor_torques = np.multiply(motor_torques, self._motor_direction)
return motor_torques
def ConvertFromLegModel(self, actions):
"""Convert the actions that use leg model to the real motor actions.
Args:
actions: The theta, phi of the leg model.
Returns:
The eight desired motor angles that can be used in ApplyActions().
"""
motor_angle = copy.deepcopy(actions)
scale_for_singularity = 1
offset_for_singularity = 1.5
half_num_motors = self.num_motors / 2
quater_pi = math.pi / 4
for i in xrange(self.num_motors):
action_idx = i // 2
forward_backward_component = (-scale_for_singularity * quater_pi * (
actions[action_idx + half_num_motors] + offset_for_singularity))
extension_component = (-1)**i * quater_pi * actions[action_idx]
if i >= half_num_motors:
extension_component = -extension_component
motor_angle[i] = (
math.pi + forward_backward_component + extension_component)
return motor_angle
def GetBaseMassFromURDF(self):
"""Get the mass of the base from the URDF file."""
return self._base_mass_urdf
def GetLegMassesFromURDF(self):
"""Get the mass of the legs from the URDF file."""
return self._leg_masses_urdf
def SetBaseMass(self, base_mass):
self._pybullet_client.changeDynamics(
self.quadruped, BASE_LINK_ID, mass=base_mass)
def SetLegMasses(self, leg_masses):
"""Set the mass of the legs.
A leg includes leg_link and motor. All four leg_links have the same mass,
which is leg_masses[0]. All four motors have the same mass, which is
leg_mass[1].
Args:
leg_masses: The leg masses. leg_masses[0] is the mass of the leg link.
leg_masses[1] is the mass of the motor.
"""
for link_id in LEG_LINK_ID:
self._pybullet_client.changeDynamics(
self.quadruped, link_id, mass=leg_masses[0])
for link_id in MOTOR_LINK_ID:
self._pybullet_client.changeDynamics(
self.quadruped, link_id, mass=leg_masses[1])
def SetFootFriction(self, foot_friction):
"""Set the lateral friction of the feet.
Args:
foot_friction: The lateral friction coefficient of the foot. This value is
shared by all four feet.
"""
for link_id in FOOT_LINK_ID:
self._pybullet_client.changeDynamics(
self.quadruped, link_id, lateralFriction=foot_friction)
def SetBatteryVoltage(self, voltage):
if self._accurate_motor_model_enabled:
self._motor_model.set_voltage(voltage)
def SetMotorViscousDamping(self, viscous_damping):
if self._accurate_motor_model_enabled:
self._motor_model.set_viscous_damping(viscous_damping)

113
examples/pybullet/gym/pybullet_envs/bullet/minitaurGymEnv.py
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import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import pybullet as p
from . import minitaur_new
class MinitaurGymEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second' : 50
}
def __init__(self,
urdfRoot="",
actionRepeat=1,
isEnableSelfCollision=True,
motorVelocityLimit=10.0,
render=False):
self._timeStep = 0.01
self._urdfRoot = urdfRoot
self._actionRepeat = actionRepeat
self._motorVelocityLimit = motorVelocityLimit
self._isEnableSelfCollision = isEnableSelfCollision
self._observation = []
self._envStepCounter = 0
self._render = render
self._lastBasePosition = [0, 0, 0]
if self._render:
p.connect(p.GUI)
else:
p.connect(p.DIRECT)
self._seed()
self.reset()
observationDim = self._minitaur.getObservationDimension()
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
actionDim = 8
action_high = np.array([1] * actionDim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(-observation_high, observation_high)
self.viewer = None
def _reset(self):
p.resetSimulation()
p.setPhysicsEngineParameter(numSolverIterations=300)
p.setTimeStep(self._timeStep)
p.loadURDF("%splane.urdf" % self._urdfRoot)
p.setGravity(0,0,-10)
self._minitaur = minitaur_new.Minitaur(urdfRootPath=self._urdfRoot, timeStep=self._timeStep, isEnableSelfCollision=self._isEnableSelfCollision, motorVelocityLimit=self._motorVelocityLimit)
self._envStepCounter = 0
self._lastBasePosition = [0, 0, 0]
for i in range(100):
p.stepSimulation()
self._observation = self._minitaur.getObservation()
return self._observation
def __del__(self):
p.disconnect()
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def _step(self, action):
if (self._render):
basePos = self._minitaur.getBasePosition()
p.resetDebugVisualizerCamera(1, 30, 40, basePos)
if len(action) != self._minitaur.getActionDimension():
raise ValueError("We expect {} continuous action not {}.".format(self._minitaur.getActionDimension(), len(action)))
for i in range(len(action)):
if not -1.01 <= action[i] <= 1.01:
raise ValueError("{}th action should be between -1 and 1 not {}.".format(i, action[i]))
realAction = self._minitaur.convertFromLegModel(action)
self._minitaur.applyAction(realAction)
for i in range(self._actionRepeat):
p.stepSimulation()
if self._render:
time.sleep(self._timeStep)
self._observation = self._minitaur.getObservation()
if self._termination():
break
self._envStepCounter += 1
reward = self._reward()
done = self._termination()
return np.array(self._observation), reward, done, {}
def _render(self, mode='human', close=False):
return
def is_fallen(self):
orientation = self._minitaur.getBaseOrientation()
rotMat = p.getMatrixFromQuaternion(orientation)
localUp = rotMat[6:]
return np.dot(np.asarray([0, 0, 1]), np.asarray(localUp)) < 0 or self._observation[-1] < 0.1
def _termination(self):
return self.is_fallen()
def _reward(self):
currentBasePosition = self._minitaur.getBasePosition()
forward_reward = currentBasePosition[0] - self._lastBasePosition[0]
self._lastBasePosition = currentBasePosition
energyWeight = 0.001
energy = np.abs(np.dot(self._minitaur.getMotorTorques(), self._minitaur.getMotorVelocities())) * self._timeStep
energy_reward = energyWeight * energy
reward = forward_reward - energy_reward
return reward

90
examples/pybullet/gym/pybullet_envs/bullet/minitaur_bullet.py
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import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import pybullet as p
from pybullet_envs.bullet.minitaur import Minitaur
class MinitaurBulletEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second' : 50
}
def __init__(self):
self._timeStep = 0.01
self._observation = []
self._envStepCounter = 0
self._lastBasePosition = [0, 0, 0]
p.connect(p.GUI)
p.resetSimulation()
p.setTimeStep(self._timeStep)
p.loadURDF("plane.urdf")
p.setGravity(0,0,-10)
self._minitaur = Minitaur()
observationDim = self._minitaur.getObservationDimension()
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
actionDim = 8
action_high = np.array([math.pi / 2.0] * actionDim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(-observation_high, observation_high)
self._seed()
self.reset()
self.viewer = None
def __del__(self):
p.disconnect()
def _seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def _step(self, action):
if len(action) != self._minitaur.getActionDimension():
raise ValueError("We expect {} continuous action not {}.".format(self._minitaur.getActionDimension(), len(actions.continuous_actions)))
for i in range(len(action)):
if not -math.pi/2 <= action[i] <= math.pi/2:
raise ValueError("{}th action should be between -1 and 1 not {}.".format(i, action[i]))
action[i] += math.pi / 2
self._minitaur.applyAction(action)
p.stepSimulation()
self._observation = self._minitaur.getObservation()
self._envStepCounter += 1
reward = self._reward()
done = self._termination()
return np.array(self._observation), reward, done, {}
def _reset(self):
p.resetSimulation()
p.setTimeStep(self._timeStep)
p.loadURDF("plane.urdf")
p.setGravity(0,0,-10)
self._minitaur = Minitaur()
self._observation = self._minitaur.getObservation()
def _render(self, mode='human', close=False):
return
def is_fallen(self):
orientation = self._minitaur.getBaseOrientation()
rotMat = p.getMatrixFromQuaternion(orientation)
localUp = rotMat[6:]
return np.dot(np.asarray([0, 0, 1]), np.asarray(localUp)) < 0
def _termination(self):
return self.is_fallen()
def _reward(self):
currentBasePosition = self._minitaur.getBasePosition()
reward = np.dot(np.asarray([-1, 0, 0]), np.asarray(currentBasePosition) - np.asarray(self._lastBasePosition))
self._lastBasePosition = currentBasePosition
return reward

346
examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py Normal file
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"""This file implements the gym environment of minitaur.
"""
import math
import time
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import pybullet
import bullet_client
import minitaur
import os
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
class MinitaurGymEnv(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=os.path.join(os.path.dirname(__file__),"../data"),
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,
leg_model_enabled=True,
accurate_motor_model_enabled=False,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=False,
hard_reset=True,
on_rack=False,
render=False,
kd_for_pd_controllers=0.3,
env_randomizer=None):
"""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._cam_pitch = -30
self._hard_reset = True
self._kd_for_pd_controllers = kd_for_pd_controllers
self._last_frame_time = 0.0
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 _reset(self):
if self._hard_reset:
self._pybullet_client.resetSimulation()
self._pybullet_client.setPhysicsEngineParameter(
numSolverIterations=self._num_bullet_solver_iterations)
self._pybullet_client.setTimeStep(self._time_step)
self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
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)
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 xrange(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 xrange(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="human", close=False):
return
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 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.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

176
examples/pybullet/gym/pybullet_envs/bullet/minitaur_new.py
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@@ -1,176 +0,0 @@
import pybullet as p
import numpy as np
import copy
import math
class Minitaur:
def __init__(self, urdfRootPath='', timeStep=0.01, isEnableSelfCollision=True, motorVelocityLimit=10.0):
self.urdfRootPath = urdfRootPath
self.isEnableSelfCollision = isEnableSelfCollision
self.motorVelocityLimit = motorVelocityLimit
self.timeStep = timeStep
self.reset()
def buildJointNameToIdDict(self):
nJoints = p.getNumJoints(self.quadruped)
self.jointNameToId = {}
for i in range(nJoints):
jointInfo = p.getJointInfo(self.quadruped, i)
self.jointNameToId[jointInfo[1].decode('UTF-8')] = jointInfo[0]
self.resetPose()
def buildMotorIdList(self):
self.motorIdList.append(self.jointNameToId['motor_front_leftL_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_leftR_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_leftL_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_leftR_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_rightL_joint'])
self.motorIdList.append(self.jointNameToId['motor_front_rightR_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_rightL_joint'])
self.motorIdList.append(self.jointNameToId['motor_back_rightR_joint'])
def reset(self):
if self.isEnableSelfCollision:
self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2], flags=p.URDF_USE_SELF_COLLISION)
else:
self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath, [0,0,.2])
self.kp = 1
self.kd = 1
self.maxForce = 3.5
self.nMotors = 8
self.motorIdList = []
self.motorDir = [-1, -1, -1, -1, 1, 1, 1, 1]
self.buildJointNameToIdDict()
self.buildMotorIdList()
def setMotorAngleById(self, motorId, desiredAngle):
p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=motorId, controlMode=p.POSITION_CONTROL, targetPosition=desiredAngle, positionGain=self.kp, velocityGain=self.kd, force=self.maxForce)
def setMotorAngleByName(self, motorName, desiredAngle):
self.setMotorAngleById(self.jointNameToId[motorName], desiredAngle)
def resetPose(self):
kneeFrictionForce = 0
halfpi = 1.57079632679
kneeangle = -2.1834 #halfpi - acos(upper_leg_length / lower_leg_length)
#left front leg
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftL_joint'],self.motorDir[0]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftL_link'],self.motorDir[0]*kneeangle)
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftR_joint'],self.motorDir[1]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftR_link'],self.motorDir[1]*kneeangle)
p.createConstraint(self.quadruped,self.jointNameToId['knee_front_leftR_link'],self.quadruped,self.jointNameToId['knee_front_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
self.setMotorAngleByName('motor_front_leftL_joint', self.motorDir[0]*halfpi)
self.setMotorAngleByName('motor_front_leftR_joint', self.motorDir[1]*halfpi)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_leftL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_leftR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
#left back leg
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftL_joint'],self.motorDir[2]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftL_link'],self.motorDir[2]*kneeangle)
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftR_joint'],self.motorDir[3]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftR_link'],self.motorDir[3]*kneeangle)
p.createConstraint(self.quadruped,self.jointNameToId['knee_back_leftR_link'],self.quadruped,self.jointNameToId['knee_back_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
self.setMotorAngleByName('motor_back_leftL_joint',self.motorDir[2]*halfpi)
self.setMotorAngleByName('motor_back_leftR_joint',self.motorDir[3]*halfpi)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_leftL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_leftR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
#right front leg
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightL_joint'],self.motorDir[4]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightL_link'],self.motorDir[4]*kneeangle)
p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightR_joint'],self.motorDir[5]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightR_link'],self.motorDir[5]*kneeangle)
p.createConstraint(self.quadruped,self.jointNameToId['knee_front_rightR_link'],self.quadruped,self.jointNameToId['knee_front_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
self.setMotorAngleByName('motor_front_rightL_joint',self.motorDir[4]*halfpi)
self.setMotorAngleByName('motor_front_rightR_joint',self.motorDir[5]*halfpi)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_rightL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_rightR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
#right back leg
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightL_joint'],self.motorDir[6]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightL_link'],self.motorDir[6]*kneeangle)
p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightR_joint'],self.motorDir[7]*halfpi)
p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightR_link'],self.motorDir[7]*kneeangle)
p.createConstraint(self.quadruped,self.jointNameToId['knee_back_rightR_link'],self.quadruped,self.jointNameToId['knee_back_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
self.setMotorAngleByName('motor_back_rightL_joint',self.motorDir[6]*halfpi)
self.setMotorAngleByName('motor_back_rightR_joint',self.motorDir[7]*halfpi)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_rightL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_rightR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
def getBasePosition(self):
position, orientation = p.getBasePositionAndOrientation(self.quadruped)
return position
def getBaseOrientation(self):
position, orientation = p.getBasePositionAndOrientation(self.quadruped)
return orientation
def getActionDimension(self):
return self.nMotors
def getObservationDimension(self):
return len(self.getObservation())
def getObservation(self):
observation = []
observation.extend(self.getMotorAngles().tolist())
observation.extend(self.getMotorVelocities().tolist())
observation.extend(self.getMotorTorques().tolist())
observation.extend(list(self.getBaseOrientation()))
return observation
def applyAction(self, motorCommands):
if self.motorVelocityLimit < np.inf:
currentMotorAngle = self.getMotorAngles()
motorCommandsMax = currentMotorAngle + self.timeStep * self.motorVelocityLimit
motorCommandsMin = currentMotorAngle - self.timeStep * self.motorVelocityLimit
motorCommands = np.clip(motorCommands, motorCommandsMin, motorCommandsMax)
motorCommandsWithDir = np.multiply(motorCommands, self.motorDir)
# print('action: {}'.format(motorCommands))
# print('motor: {}'.format(motorCommandsWithDir))
for i in range(self.nMotors):
self.setMotorAngleById(self.motorIdList[i], motorCommandsWithDir[i])
def getMotorAngles(self):
motorAngles = []
for i in range(self.nMotors):
jointState = p.getJointState(self.quadruped, self.motorIdList[i])
motorAngles.append(jointState[0])
motorAngles = np.multiply(motorAngles, self.motorDir)
return motorAngles
def getMotorVelocities(self):
motorVelocities = []
for i in range(self.nMotors):
jointState = p.getJointState(self.quadruped, self.motorIdList[i])
motorVelocities.append(jointState[1])
motorVelocities = np.multiply(motorVelocities, self.motorDir)
return motorVelocities
def getMotorTorques(self):
motorTorques = []
for i in range(self.nMotors):
jointState = p.getJointState(self.quadruped, self.motorIdList[i])
motorTorques.append(jointState[3])
motorTorques = np.multiply(motorTorques, self.motorDir)
return motorTorques
def convertFromLegModel(self, actions):
motorAngle = copy.deepcopy(actions)
scaleForSingularity = 1
offsetForSingularity = 0.5
motorAngle[0] = math.pi + math.pi / 4 * actions[0] - scaleForSingularity * math.pi / 4 * (actions[4] + 1 + offsetForSingularity)
motorAngle[1] = math.pi - math.pi / 4 * actions[0] - scaleForSingularity * math.pi / 4 * (actions[4] + 1 + offsetForSingularity)
motorAngle[2] = math.pi + math.pi / 4 * actions[1] - scaleForSingularity * math.pi / 4 * (actions[5] + 1 + offsetForSingularity)
motorAngle[3] = math.pi - math.pi / 4 * actions[1] - scaleForSingularity * math.pi / 4 * (actions[5] + 1 + offsetForSingularity)
motorAngle[4] = math.pi - math.pi / 4 * actions[2] - scaleForSingularity * math.pi / 4 * (actions[6] + 1 + offsetForSingularity)
motorAngle[5] = math.pi + math.pi / 4 * actions[2] - scaleForSingularity * math.pi / 4 * (actions[6] + 1 + offsetForSingularity)
motorAngle[6] = math.pi - math.pi / 4 * actions[3] - scaleForSingularity * math.pi / 4 * (actions[7] + 1 + offsetForSingularity)
motorAngle[7] = math.pi + math.pi / 4 * actions[3] - scaleForSingularity * math.pi / 4 * (actions[7] + 1 + offsetForSingularity)
return motorAngle

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examples/pybullet/gym/pybullet_envs/bullet/motor.py Normal file
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"""This file implements an accurate motor model."""
import numpy as np
VOLTAGE_CLIPPING = 50
OBSERVED_TORQUE_LIMIT = 5.7
MOTOR_VOLTAGE = 16.0
MOTOR_RESISTANCE = 0.186
MOTOR_TORQUE_CONSTANT = 0.0954
MOTOR_VISCOUS_DAMPING = 0
MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING
+ MOTOR_TORQUE_CONSTANT)
class MotorModel(object):
"""The accurate motor model, which is based on the physics of DC motors.
The motor model support two types of control: position control and torque
control. In position control mode, a desired motor angle is specified, and a
torque is computed based on the internal motor model. When the torque control
is specified, a pwm signal in the range of [-1.0, 1.0] is converted to the
torque.
The internal motor model takes the following factors into consideration:
pd gains, viscous friction, back-EMF voltage and current-torque profile.
"""
def __init__(self,
torque_control_enabled=False,
kp=1.2,
kd=0):
self._torque_control_enabled = torque_control_enabled
self._kp = kp
self._kd = kd
self._resistance = MOTOR_RESISTANCE
self._voltage = MOTOR_VOLTAGE
self._torque_constant = MOTOR_TORQUE_CONSTANT
self._viscous_damping = MOTOR_VISCOUS_DAMPING
self._current_table = [0, 10, 20, 30, 40, 50, 60]
self._torque_table = [0, 1, 1.9, 2.45, 3.0, 3.25, 3.5]
def set_voltage(self, voltage):
self._voltage = voltage
def get_voltage(self):
return self._voltage
def set_viscous_damping(self, viscous_damping):
self._viscous_damping = viscous_damping
def get_viscous_dampling(self):
return self._viscous_damping
def convert_to_torque(self, motor_commands, current_motor_angle,
current_motor_velocity):
"""Convert the commands (position control or torque control) to torque.
Args:
motor_commands: The desired motor angle if the motor is in position
control mode. The pwm signal if the motor is in torque control mode.
current_motor_angle: The motor angle at the current time step.
current_motor_velocity: The motor velocity at the current time step.
Returns:
actual_torque: The torque that needs to be applied to the motor.
observed_torque: The torque observed by the sensor.
"""
if self._torque_control_enabled:
pwm = motor_commands
else:
pwm = (-self._kp * (current_motor_angle - motor_commands)
- self._kd * current_motor_velocity)
pwm = np.clip(pwm, -1.0, 1.0)
return self._convert_to_torque_from_pwm(pwm, current_motor_velocity)
def _convert_to_torque_from_pwm(self, pwm, current_motor_velocity):
"""Convert the pwm signal to torque.
Args:
pwm: The pulse width modulation.
current_motor_velocity: The motor velocity at the current time step.
Returns:
actual_torque: The torque that needs to be applied to the motor.
observed_torque: The torque observed by the sensor.
"""
observed_torque = np.clip(
self._torque_constant * (pwm * self._voltage / self._resistance),
-OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT)
# Net voltage is clipped at 50V by diodes on the motor controller.
voltage_net = np.clip(pwm * self._voltage -
(self._torque_constant + self._viscous_damping)
* current_motor_velocity,
-VOLTAGE_CLIPPING, VOLTAGE_CLIPPING)
current = voltage_net / self._resistance
current_sign = np.sign(current)
current_magnitude = np.absolute(current)
# Saturate torque based on empirical current relation.
actual_torque = np.interp(current_magnitude, self._current_table,
self._torque_table)
actual_torque = np.multiply(current_sign, actual_torque)
return actual_torque, observed_torque