add actuatornet code

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py

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+
+

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py

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+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from absl import app
+from absl import flags
+from keras.callbacks import ModelCheckpoint
+from keras.layers import Dense
+from keras.models import Sequential
+from keras import optimizers
+import numpy
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+    "input_filename", "data/minitaur_log_latency_0.01.csv", "The name of the input CSV file."
+    "Each line in the CSV file will contain the motor position, the "
+    "motor speed, action and torques.")
+
+
+def main(unused_argv):
+  # fix random seed for reproducibility
+  numpy.random.seed(7)
+  # load pima indians dataset
+  dataset = numpy.loadtxt(
+      FLAGS.input_filename,
+      delimiter=",")
+  # split into input (X) and output (Y) variables
+  x = dataset[:, 0:3]
+  y = dataset[:, 3]
+  print("x=", x)
+  print("y=", y)
+
+  # create model
+  model = Sequential()
+  model.add(Dense(12, input_dim=3, activation="relu"))
+  model.add(Dense(8, activation="sigmoid"))
+  model.add(Dense(1, activation="linear"))
+
+  # Compile model (use adam or sgd)
+  model.compile(
+      loss="mean_squared_error",
+      optimizer="adam",
+      metrics=["mean_squared_error"])
+
+  # checkpoint
+  filepath = "/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
+  checkpoint = ModelCheckpoint(
+      filepath, monitor="val_loss", verbose=1, save_best_only=True, mode="min")
+  callbacks_list = [checkpoint]
+
+  # Fit the model
+  # model.fit(X, Y, epochs=150, batch_size=10)
+  # model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list)
+  model.fit(
+      x,
+      y,
+      validation_split=0.34,
+      epochs=4500,
+      batch_size=1024,
+      callbacks=callbacks_list,
+      verbose=0)
+
+  # evaluate the model
+  scores = model.evaluate(x, y)
+  print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
+
+
+if __name__ == "__main__":
+  app.run(main)

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py

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+"""see https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/"""
+
+import matplotlib
+matplotlib.use('TkAgg')
+import numpy as np
+from matplotlib import pyplot
+from pandas import read_csv
+from pandas import DataFrame
+from pandas import concat
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import mean_squared_error
+from keras.models import Sequential
+from keras.layers import Dropout
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.callbacks import ModelCheckpoint
+from matplotlib import pyplot
+
+from pandas import read_csv
+
+
+# convert series to supervised learning
+def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
+  n_vars = 1 if type(data) is list else data.shape[1]
+  df = DataFrame(data)
+  cols, names = list(), list()
+  # input sequence (t-n, ... t-1)
+  for i in range(n_in, 0, -1):
+    cols.append(df.shift(i))
+    names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
+  # forecast sequence (t, t+1, ... t+n)
+  for i in range(0, n_out):
+    cols.append(df.shift(-i))
+    if i == 0:
+      names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
+    else:
+      names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
+  # put it all together
+  agg = concat(cols, axis=1)
+  agg.columns = names
+  # drop rows with NaN values
+  if dropnan:
+    agg.dropna(inplace=True)
+  return agg
+
+
+values = [x for x in range(10)]
+data = series_to_supervised(values, 2)
+print(data)
+
+# load dataset
+#dataset = read_csv('./data/minitaur_log_latency_0.01.csv')
+#dataset = read_csv('./data/minitaur_log_latency_0.003.csv')
+dataset = read_csv('./data/minitaur_log_latency_0.006.csv')
+
+values = dataset.values
+# integer encode direction
+#encoder = LabelEncoder()
+#values[:,3] = encoder.fit_transform(values[:,3])
+# ensure all data is float
+values = values.astype('float32')
+
+# normalize features
+useNormalization = False
+if useNormalization:
+  scaler = MinMaxScaler(feature_range=(0, 1))
+  scaled = scaler.fit_transform(values)
+else:
+  scaled = values
+
+# frame as supervised learning
+lag_steps = 5
+reframed = series_to_supervised(scaled, lag_steps, 1)
+print("reframed before drop=", reframed)
+
+# drop columns we don't want to predict
+reframed.drop(reframed.columns[[3,7,11,15,19]], axis=1, inplace=True)
+print("after drop=",reframed.head())
+
+#dummy = scaler.inverse_transform(reframed)
+#print(dummy)
+
+groups = [0, 1, 2, 3]
+
+i = 1
+# plot each column
+doPlot = False
+if doPlot:
+  pyplot.figure()
+  for group in groups:
+    pyplot.subplot(len(groups), 1, i)
+    pyplot.plot(values[0:25, group])
+    pyplot.title(dataset.columns[group], y=0.5, loc='right')
+    i += 1
+  pyplot.show()
+
+# split into train and test sets
+values = reframed.values
+n_train_hours = 6000
+train = values[:n_train_hours, :]
+test = values[n_train_hours:, :]
+# split into input and outputs
+train_X, train_y = train[:, :-1], train[:, -1]
+test_X, test_y = test[:, :-1], test[:, -1]
+
+print("train_X.shape[1]=",train_X.shape[1])
+
+
+# design network
+useLSTM=True
+if useLSTM:
+  # reshape input to be 3D [samples, timesteps, features]
+  train_X = train_X.reshape((train_X.shape[0], lag_steps+1, int(train_X.shape[1]/(lag_steps+1))))
+  test_X = test_X.reshape((test_X.shape[0], lag_steps+1, int(test_X.shape[1]/(lag_steps+1))))
+  model = Sequential()
+  model.add(LSTM(40,  input_shape=(train_X.shape[1], train_X.shape[2])))
+  model.add(Dropout(0.05))
+  model.add(Dense(8, activation='sigmoid'))
+  model.add(Dense(8, activation='sigmoid'))
+  model.add(Dropout(0.05))
+  model.add(Dense(1, activation='linear'))
+else:
+  # create model
+  model = Sequential()
+  model.add(Dense(12, input_dim=train_X.shape[1], activation="relu"))
+  model.add(Dense(8, activation="sigmoid"))
+  model.add(Dense(1, activation="linear"))
+
+#model.compile(loss='mae', optimizer='adam')
+model.compile(
+    loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error'])
+
+# checkpoint
+filepath = '/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5'
+checkpoint = ModelCheckpoint(
+    filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
+callbacks_list = [checkpoint]
+
+# fit network
+history = model.fit(
+    train_X,
+    train_y,
+    epochs=1500,
+    batch_size=32,
+    callbacks=callbacks_list,
+    validation_data=(test_X, test_y),
+    verbose=2,
+    shuffle=False)
+# plot history
+
+
+data = np.array([[[1.513535008329887299,3.234624992847829894e-01,1.731481043119239782,1.741165415165205399,
+1.534267104753672228e+00,1.071354965017878635e+00,1.712386127673626302e+00]]])
+
+
+#prediction = model.predict(data)
+#print("prediction=",prediction)
+
+
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py

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+#The example to run the raibert controller in a Minitaur gym env.
+
+#blaze run :minitaur_raibert_controller_example  -- --log_path=/tmp/logs
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from pybullet_envs.minitaur.envs import minitaur_raibert_controller
+from pybullet_envs.minitaur.envs import minitaur_gym_env
+
+flags = tf.app.flags
+FLAGS = tf.app.flags.FLAGS
+
+flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.")
+flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.")
+flags.DEFINE_float(
+    "control_latency", 0.006, "The latency between sensor measurement and action"
+    " execution the robot.")
+flags.DEFINE_string("log_path", ".", "The directory to write the log file.")
+
+
+def speed(t):
+  max_speed = 0.35
+  t1 = 3
+  if t < t1:
+    return t / t1 * max_speed
+  else:
+    return -max_speed
+
+
+def main(argv):
+    del argv
+    env = minitaur_gym_env.MinitaurGymEnv(
+        urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
+        control_time_step=0.006,
+        action_repeat=6,
+        pd_latency=0.0,
+        control_latency=FLAGS.control_latency,
+        motor_kp=FLAGS.motor_kp,
+        motor_kd=FLAGS.motor_kd,
+        remove_default_joint_damping=True,
+        leg_model_enabled=False,
+        render=True,
+        on_rack=False,
+        accurate_motor_model_enabled=True,
+        log_path=FLAGS.log_path)
+    env.reset()
+
+    controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(
+        env.minitaur)
+
+    tstart = env.minitaur.GetTimeSinceReset()
+    for _ in range(1000):
+      t = env.minitaur.GetTimeSinceReset() - tstart
+      controller.behavior_parameters = (
+          minitaur_raibert_controller.BehaviorParameters(
+              desired_forward_speed=speed(t)))
+      controller.update(t)
+      env.step(controller.get_action())
+
+    #env.close()
+
+if __name__ == "__main__":
+  tf.app.run(main)
+

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+#python proto2csv.py --proto_file=/tmp/logs/minitaur_log_2019-01-27-12-59-31 --csv_file=/tmp/logs/out.csv
+#each line in csv contains: angle, velocity, action, torque
+
+import tensorflow as tf
+import argparse
+import numpy
+from pybullet_envs.minitaur.envs import minitaur_logging
+
+flags = tf.app.flags
+FLAGS = tf.app.flags.FLAGS
+
+flags.DEFINE_string("proto_file", "logs", "path to protobuf input file")
+flags.DEFINE_string("csv_file", "file.csv", "poth to csv output file")
+
+
+def main(argv):
+  del argv
+
+  logging = minitaur_logging.MinitaurLogging()
+  episode = logging.restore_episode(FLAGS.proto_file)
+  #print(dir (episode))
+  #print("episode=",episode)
+  fields = episode.ListFields()
+
+  recs = []
+
+  for rec in fields[0][1]:
+    #print(rec.time)
+    for motorState in rec.motor_states:
+      #print("motorState.angle=",motorState.angle)
+      #print("motorState.velocity=",motorState.velocity)
+      #print("motorState.action=",motorState.action)
+      #print("motorState.torque=",motorState.torque)
+      recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque])
+		
+  a = numpy.array(recs)
+  numpy.savetxt(FLAGS.csv_file, a, delimiter=",")
+
+if __name__ == "__main__":
+  tf.app.run(main)
+
+

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/requirements.txt

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+pybullet
+tensorflow
+gym
+pandas
+matplotlib
+

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py

@@ -249,7 +249,8 @@ class MinitaurGymEnv(gym.Env):
     self._hard_reset = hard_reset  # This assignment need to be after reset()
 
   def close(self):
-    self.logging.save_episode(self._episode_proto)
+    if self._env_step_counter>0:
+      self.logging.save_episode(self._episode_proto)
     self.minitaur.Terminate()
 
   def add_env_randomizer(self, env_randomizer):
@@ -258,7 +259,8 @@ class MinitaurGymEnv(gym.Env):
   def reset(self, initial_motor_angles=None, reset_duration=1.0):
     self._pybullet_client.configureDebugVisualizer(
         self._pybullet_client.COV_ENABLE_RENDERING, 0)
-    self.logging.save_episode(self._episode_proto)
+    if self._env_step_counter>0:
+      self.logging.save_episode(self._episode_proto)
     self._episode_proto = minitaur_logging_pb2.MinitaurEpisode()
     minitaur_logging.preallocate_episode_proto(self._episode_proto,
                                                self._num_steps_to_log)