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use cnn_to_mlp to allow training of racecar using (extremely simplified) ZED camera pixel data using OpenAI baselines.

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add a red sphere to make training a bit easier for now.
This commit is contained in:
Erwin Coumans 2017-06-13 18:33:32 -07:00
parent ee8fd56c5e
commit a0ded43a69
6 changed files with 3793 additions and 29 deletions
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32
data/sphere2red.urdf Normal file
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@ -0,0 +1,32 @@
<?xml version="0.0" ?>
<robot name="urdf_robot">
<link name="baseLink">
<contact>
<rolling_friction value="0.03"/>
<spinning_friction value="0.03"/>
</contact>
<inertial>
<origin rpy="0 0 0" xyz="0 0 0"/>
<mass value="10.0"/>
<inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
</inertial>
<visual>
<origin rpy="0 0 0" xyz="0 0 0"/>
<geometry>
<mesh filename="sphere_smooth.obj" scale="0.5 0.5 0.5"/>
</geometry>
<material name="red">
<color rgba="1 0.2 0.2 1"/>
<specular rgb="1 1 1"/>
</material>
</visual>
<collision>
<origin rpy="0 0 0" xyz="0 0 0"/>
<geometry>
<sphere radius="0.5"/>
</geometry>
</collision>
</link>
</robot>

10
data/sphere_smooth.mtl Normal file
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@ -0,0 +1,10 @@
# Blender MTL File: 'None'
# Material Count: 1
newmtl None
Ns 0
Ka 0.000000 0.000000 0.000000
Kd 1. 0.2 0.2
Ks 0.8 0.8 0.8
d 1
illum 2

3725
data/sphere_smooth.obj Normal file
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File diff suppressed because it is too large Load Diff

2
examples/pybullet/gym/enjoy_pybullet_zed_racecar.py
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@ -7,7 +7,7 @@ from baselines import deepq
def main():
env = RacecarZEDGymEnv(renders=True)
act = deepq.load("racecar_model.pkl")
act = deepq.load("racecar_zed_model.pkl")
print(act)
while True:
obs, done = env.reset(), False

42
examples/pybullet/gym/envs/bullet/racecarZEDGymEnv.py
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@ -16,7 +16,7 @@ class RacecarZEDGymEnv(gym.Env):
def __init__(self,
urdfRoot="",
actionRepeat=50,
actionRepeat=100,
isEnableSelfCollision=True,
renders=True):
print("init")
@ -24,10 +24,11 @@ class RacecarZEDGymEnv(gym.Env):
self._urdfRoot = urdfRoot
self._actionRepeat = actionRepeat
self._isEnableSelfCollision = isEnableSelfCollision
self._observation = []
self._ballUniqueId = -1
self._envStepCounter = 0
self._renders = renders
self._width = 100
self._height = 10
self._p = p
if self._renders:
p.connect(p.GUI)
@ -41,7 +42,8 @@ class RacecarZEDGymEnv(gym.Env):
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
self.action_space = spaces.Discrete(6)
self.observation_space = spaces.Box(-observation_high, observation_high)
self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4))
self.viewer = None
def _reset(self):
@ -63,7 +65,7 @@ class RacecarZEDGymEnv(gym.Env):
bally = dist * math.cos(ang)
ballz = 1
self._ballUniqueId = p.loadURDF("sphere2.urdf",[ballx,bally,ballz])
self._ballUniqueId = p.loadURDF("sphere2red.urdf",[ballx,bally,ballz])
p.setGravity(0,0,-10)
self._racecar = racecar.Racecar(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
self._envStepCounter = 0
@ -80,7 +82,6 @@ class RacecarZEDGymEnv(gym.Env):
return [seed]
def getExtendedObservation(self):
self._observation = [] #self._racecar.getObservation()
carpos,carorn = p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
carmat = p.getMatrixFromQuaternion(carorn)
ballpos,ballorn = p.getBasePositionAndOrientation(self._ballUniqueId)
@ -88,25 +89,18 @@ class RacecarZEDGymEnv(gym.Env):
ballPosInCar,ballOrnInCar = p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
dist0 = 0.3
dist1 = 1.
if self._renders:
print("carpos")
print(carpos)
eyePos = carpos
print("carmat012")
print(carmat[0],carmat[1],carmat[2])
print("carmat345")
print(carmat[3],carmat[4],carmat[5])
print("carmat678")
print(carmat[6],carmat[7],carmat[8])
eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3]
targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
up = [0,0,1]
viewMat = p.computeViewMatrix(eyePos,targetPos,up)
#viewMat = p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
p.getCameraImage(width=320,height=240,viewMatrix=viewMat,projectionMatrix=p.getDebugVisualizerCamera()[3],renderer=p.ER_BULLET_HARDWARE_OPENGL)
self._observation.extend([ballPosInCar[0],ballPosInCar[1]])
eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3]
targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
up = [carmat[2],carmat[5],carmat[8]]
viewMat = p.computeViewMatrix(eyePos,targetPos,up)
#viewMat = p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
#print("projectionMatrix:")
#print(p.getDebugVisualizerCamera()[3])
projMatrix = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
rgb=img_arr[2]
np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
self._observation = np_img_arr
return self._observation
def _step(self, action):

11
examples/pybullet/gym/train_pybullet_zed_racecar.py
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@ -14,11 +14,14 @@ def callback(lcl, glb):
is_solved = totalt > 2000 and total >= -50
return is_solved
def main():
env = RacecarZEDGymEnv(renders=False)
model = deepq.models.mlp([64])
model = deepq.models.cnn_to_mlp(
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=False
)
act = deepq.learn(
env,
q_func=model,
@ -30,8 +33,8 @@ def main():
print_freq=10,
callback=callback
)
print("Saving model to racecar_model.pkl")
act.save("racecar_model.pkl")
print("Saving model to racecar_zed_model.pkl")
act.save("racecar_zed_model.pkl")
if __name__ == '__main__':