bullet3/examples/pybullet/tensorflow/humanoid_running_vr_follow.py

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import tensorflow as tf
import sys
import numpy as np
import pybullet as p
import time
p.connect(p.SHARED_MEMORY) #DIRECT is much faster, but GUI shows the running gait
p.resetSimulation()
p.setGravity(0,0,-9.8)
p.setRealTimeSimulation(0)
p.setPhysicsEngineParameter(fixedTimeStep=1.0/60., numSolverIterations=5, numSubSteps=2)
#this mp4 recording requires ffmpeg installed
#mp4log = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,"humanoid.mp4")
plane, human = p.loadMJCF("mjcf/humanoid_symmetric.xml",flags = p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
print("human")
print(human)
p.setVRCameraState([0,0,0],[0,0,0,0],human, trackObjectFlag=human)#p.VR_CAMERA_TRACK_OBJECT_ORIENTATION)
ordered_joints = []
ordered_joint_indices = []
jdict = {}
for j in range( p.getNumJoints(human) ):
info = p.getJointInfo(human, j)
link_name = info[12].decode("ascii")
if link_name=="left_foot": left_foot = j
if link_name=="right_foot": right_foot = j
ordered_joint_indices.append(j)
if info[2] != p.JOINT_REVOLUTE: continue
jname = info[1].decode("ascii")
jdict[jname] = j
lower, upper = (info[8], info[9])
ordered_joints.append( (j, lower, upper) )
p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
motor_power = [100, 100, 100]
motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
motor_power += [100, 100, 300, 200]
motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
motor_power += [100, 100, 300, 200]
motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"]
motor_power += [75, 75, 75]
motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
motor_power += [75, 75, 75]
motors = [jdict[n] for n in motor_names]
class Dummy:
pass
dummy = Dummy()
dummy.initial_z = None
def current_relative_position(jointStates, human, j, lower, upper):
#print("j")
#print(j)
#print (len(jointStates))
#print(j)
temp = jointStates[j]
pos = temp[0]
vel = temp[1]
#print("pos")
#print(pos)
#print("vel")
#print(vel)
pos_mid = 0.5 * (lower + upper);
return (
2 * (pos - pos_mid) / (upper - lower),
0.1 * vel
)
def collect_observations(human):
#print("ordered_joint_indices")
#print(ordered_joint_indices)
jointStates = p.getJointStates(human,ordered_joint_indices)
j = np.array([current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints]).flatten()
#print("j")
#print(j)
body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
#print("body_xyz")
#print(body_xyz, qx,qy,qz,qw)
z = body_xyz[2]
dummy.distance = body_xyz[0]
if dummy.initial_z==None:
dummy.initial_z = z
(vx, vy, vz), _ = p.getBaseVelocity(human)
more = np.array([z-dummy.initial_z, 0.1*vx, 0.1*vy, 0.1*vz, qx, qy, qz, qw])
rcont = p.getContactPoints(human, -1, right_foot, -1)
#print("rcont")
#print(rcont)
lcont = p.getContactPoints(human, -1, left_foot, -1)
#print("lcont")
#print(lcont)
feet_contact = np.array([len(rcont)>0, len(lcont)>0])
return np.clip( np.concatenate([more] + [j] + [feet_contact]), -5, +5)
class SmallReactivePolicy(object):
def __init__(self, name, ob_space, ac_space):
self.name = name
with tf.variable_scope(name):
batch_of_observations = [None] + list(ob_space.shape)
self.ob = tf.placeholder(dtype=tf.float32, shape=batch_of_observations, name="ob")
x = self.ob
width = 256
self.dense1_w = tf.get_variable("dense1_w", [x.get_shape()[1], width])
self.dense1_b = tf.get_variable("dense1_b", [width])
x = tf.nn.relu(tf.matmul(x, self.dense1_w) + self.dense1_b)
width = 128
self.dense2_w = tf.get_variable("dense2_w", [x.get_shape()[1], width])
self.dense2_b = tf.get_variable("dense2_b", [width])
x = tf.nn.relu(tf.matmul(x, self.dense2_w) + self.dense2_b)
assert(len(ac_space.shape)==1)
width = ac_space.shape[0]
self.final_w = tf.get_variable("final_w", [x.get_shape()[1], width])
self.final_b = tf.get_variable("final_b", [width])
x = tf.matmul(x, self.final_w) + self.final_b
self.action_node = x
self.all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
def act(self, ob):
a = tf.get_default_session().run(self.action_node, feed_dict={ self.ob: ob[None] })
return a[0]
def load_weights(self):
assigns = [
(self.dense1_w, weights_dense1_w),
(self.dense1_b, weights_dense1_b),
(self.dense2_w, weights_dense2_w),
(self.dense2_b, weights_dense2_b),
(self.final_w, weights_final_w),
(self.final_b, weights_final_b)]
to_run = []
feed_dict = {}
for var, w in assigns:
ph = tf.placeholder(tf.float32, w.shape)
to_run.append( tf.assign(var, ph) )
feed_dict[ph] = w
tf.get_default_session().run(to_run, feed_dict=feed_dict)
def demo_run():
sess = tf.InteractiveSession(config=tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, device_count={ "GPU": 0 }))
pi = SmallReactivePolicy("pi", np.zeros((44,)), np.zeros((17,)))
pi.load_weights()
t1 = time.time()
timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
frame = 0
while 1:
obs = collect_observations(human)
actions = pi.act(obs)
#print(" ".join(["%+0.2f"%x for x in obs]))
#print("Motors")
#print(motors)
#for m in range(len(motors)):
#print("motor_power")
#print(motor_power[m])
#print("actions[m]")
#print(actions[m])
#p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
#p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
forces = [0.] * len(motors)
for m in range(len(motors)):
forces[m] = motor_power[m]*actions[m]*0.082
p.setJointMotorControlArray(human, motors,controlMode=p.TORQUE_CONTROL, forces=forces)
p.stepSimulation()
time.sleep(0.01)
distance=5
yaw = 0
humanPos, humanOrn = p.getBasePositionAndOrientation(human)
#p.resetDebugVisualizerCamera(distance,yaw,20,humanPos);
frame += 1
if frame==1000: break
t2 = time.time()
print("############################### distance = %0.2f meters" % dummy.distance)
print("############################### FPS = ", 1000/ (t2-t1))
#print("Starting benchmark")
#logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
#p.stopStateLogging(logId)
print("ended benchmark")
print(frame)
p.stopStateLogging(timinglog)
weights_dense1_w = np.array([
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weights_dense2_b = np.array([ +0.0589, +0.2417, -0.0986, +0.1092, +0.0381, +0.2114, +0.2297, +0.1468, -0.1559, -0.1759, +0.0344, +0.3008, +0.3244, +0.3597, +0.1691, +0.0277, +0.0064, +0.2372, +0.0961, +0.3293, +0.2077, +0.1230, +0.1195, +0.4294, +0.2816, +0.1169, +0.4083, +0.0309, +0.0433, +0.4852, +0.1903, +0.1055, +0.2894, +0.0468, +0.0669, +0.1655, -0.0712, +0.2791, +0.3653, +0.1661, +0.0730, +0.4481, +0.2336, +0.4547, +0.3048, +0.3674, +0.1072, +0.2519, +0.0875, +0.1864, +0.0793, +0.1467, +0.1665, -0.0635, +0.2464, +0.2529, +0.2301, +0.3551, +0.1974, +0.5505, +0.1776, +0.0702, -0.0460, +0.1697, -0.0007, +0.0430, +0.2190, +0.3566, +0.2163, +0.2470, +0.1928, -0.0373, +0.3103, +0.1705, +0.0386, +0.1008, +0.0483, -0.0530, +0.0899, +0.1354, +0.2905, +0.0686, +0.2512, +0.4373, +0.2106, +0.2685, +0.2320, +0.0750, +0.4447, +0.2390, -0.0136, +0.0224, +0.1565, -0.0579, -0.0142, +0.0710, +0.2252, +0.1527, +0.3315, +0.1419, +0.3483, +0.3999, +0.2818, +0.1021, +0.2659, +0.2742, +0.0814, +0.0849, -0.0068, +0.1339, +0.1216, +0.0148, +0.0219, -0.0728, +0.2957, +0.0854, +0.0055, -0.0289, +0.0866, +0.0203, +0.0353, +0.2069, +0.3297, +0.1171, +0.0607, +0.1667, +0.5721, -0.0218])
weights_final_w = np.array([
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])
weights_final_b = np.array([ -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643, +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276, +0.0375])
if __name__=="__main__":
demo_run()