diff --git a/controllers/g2p-closed-loop/.DS_Store b/controllers/g2p-closed-loop/.DS_Store
new file mode 100644
index 0000000..b8001ac
Binary files /dev/null and b/controllers/g2p-closed-loop/.DS_Store differ
diff --git a/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_large_dirty.csv b/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_large_dirty.csv
new file mode 100644
index 0000000..ab6a73d
--- /dev/null
+++ b/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_large_dirty.csv
@@ -0,0 +1,271718 @@
+tick,angle,safety_switch_pressed,flex_myobrick_pos_encoder,flex_myobrick_torque_encoder,flex_myobrick_current,flex_myobrick_pwm,flex_myobrick_in_running_state,extend_myobrick_pos_encoder,extend_myobrick_torque_encoder,extend_myobrick_current,extend_myobrick_pwm,extend_myobrick_in_running_state
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diff --git a/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_small_dirty.csv b/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_small_dirty.csv
new file mode 100644
index 0000000..7f47cb2
--- /dev/null
+++ b/controllers/g2p-closed-loop/datasets/motor_babbling_dataset_small_dirty.csv
@@ -0,0 +1,11564 @@
+tick,angle,safety_switch_pressed,flex_myobrick_pos_encoder,flex_myobrick_torque_encoder,flex_myobrick_current,flex_myobrick_pwm,flex_myobrick_in_running_state,extend_myobrick_pos_encoder,extend_myobrick_torque_encoder,extend_myobrick_current,extend_myobrick_pwm,extend_myobrick_in_running_state
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diff --git a/controllers/g2p-closed-loop/notebooks/Colab.ipynb b/controllers/g2p-closed-loop/notebooks/Colab.ipynb
new file mode 100644
index 0000000..76fcae1
--- /dev/null
+++ b/controllers/g2p-closed-loop/notebooks/Colab.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["I-ffRZ0UW1eE","RKMt1nBByjv7","z3CY20seFUFT"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# **G2P Algorithm for Roboy NTC**\n","Please check https://miro.com/app/board/uXjVP2DvcmU=/ to get an overview\n","\n","---\n","\n"],"metadata":{"id":"9UbrvXwr-ZVC"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cjwjxX1Ban3x","executionInfo":{"status":"ok","timestamp":1679345661649,"user_tz":-60,"elapsed":31090,"user":{"displayName":"Marcel Kück","userId":"09092862230391330585"}},"outputId":"d500820c-73c1-4c1c-e431-330937eaf7ee"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"XfOFbWpn4jT1","executionInfo":{"status":"ok","timestamp":1679345831092,"user_tz":-60,"elapsed":3968,"user":{"displayName":"Marcel Kück","userId":"09092862230391330585"}}},"outputs":[],"source":["#Imports\n","import tensorflow as tf\n","from tensorflow.keras import regularizers\n","import numpy as np\n","import pandas as pd\n","import sklearn\n","from scipy import signal\n","from matplotlib import pyplot as plt\n","from sklearn.neural_network import MLPRegressor\n","from sklearn.preprocessing import MinMaxScaler"]},{"cell_type":"code","source":["#prepare data for training\n","df = pd.read_csv('/content/drive/MyDrive/Roboy/bench_data_2.csv')\n","\n","#simple dataset: similar to Ali's approach\n","kinematics = df[['angle', 'angular_vel', 'angular_acc']].values\n","\n","#extended dataset with more features for kinematics data #TODO: Eliminate unessesary features \n","kinematics_extended = df[[\n","    'timestamp',\n","    'angle',\n","    'kill_switch_pressed',\n","    'flex_pv_pos_encoder',\n","    'flex_pv_torque_encoder',\n","    'flex_pv_current',\n","    'flex_sp_pwm',\n","    'flex_active',\n","    'extend_pv_pos_encoder',\n","    'extend_pv_torque_encoder',\n","    'extend_pv_current',\n","    'extend_sp_pwm',\n","    'extend_active',\n","    'angular_acc',\n","    'angular_vel'\n","]].values\n","\n","activations = df[['flex_sp_pwm','extend_sp_pwm']].values"],"metadata":{"id":"49aOVmmGb9We","executionInfo":{"status":"ok","timestamp":1679346456808,"user_tz":-60,"elapsed":408,"user":{"displayName":"Marcel Kück","userId":"09092862230391330585"}}},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":["# **Inverse Kinematics (Solving for non-linear regression problem)**"],"metadata":{"id":"tvBusvHryU8i"}},{"cell_type":"code","source":["#Training Data\n","x = kinematics\n","y = activations\n","\n","#Scale Data for training\n","scalerIn = MinMaxScaler()\n","scalerOut = MinMaxScaler()\n","x_scaled = scalerIn.fit_transform(x)\n","y_scaled = scalerOut.fit_transform(y)\n","\n","#Split unscaled data \n","x_train, x_valid, y_train, y_valid = sklearn.model_selection.train_test_split(x, y, test_size=0.2)\n","\n","#Split scaled data\n","x_train_scaled, x_valid_scaled, y_train_scaled, y_valid_scaled = sklearn.model_selection.train_test_split(x_scaled, y_scaled, test_size=0.2)"],"metadata":{"id":"hqs1_ZZIfJQk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Approach 0: MLP to solve regression problem (Ali's implementation without significant changes)\n","def inverse_mapping_fcn0():\n","\n","  model = tf.keras.Sequential()\n","  # Adds a densely-connected layer with 15 units to the model:\n","  model.add(tf.keras.layers.Dense(15, activation='relu'))\n","  # Add a softmax layer with 3 output units:\n","  model.add(tf.keras.layers.Dense(2, activation='sigmoid'))\n","  model.compile(optimizer=tf.keras.optimizers.SGD(),\n","              loss='mse',       # mean squared error\n","              metrics=['mse'])  # mean squared error\n","  #training the model\n","  history = \\\n","  model.fit(\n","  x_train_scaled,\n","  y_train_scaled,\n","  epochs=20,\n","  validation_data=(x_valid_scaled, y_valid_scaled))\n","\n","  return (model, history)\n","  \n","\n","def inverse_mapping_fcn0_1():\n","  model = MLPRegressor(\n","      hidden_layer_sizes=15,\n","      activation=\"logistic\",\n","      verbose=True,\n","      warm_start=True,\n","      early_stopping=True)\n","\n","  history = model.fit(x_train_scaled, y_train_scaled)\n"," \n","  return (model, history)"],"metadata":{"id":"8UgbFiNTFQM_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"################################## Model 0 (Ali's Model) ##################################\")\n","\n","[model0, history0] = inverse_mapping_fcn0()\n","\n","# Get the validation loss from the model's history\n","val_loss = history0.history['val_loss']\n","\n","# Create a figure and axes\n","fig, ax = plt.subplots()\n","\n","# Plot the validation loss\n","ax.plot(val_loss, label='Validation Loss')\n","\n","# Set the x-axis label\n","ax.set_xlabel('Epoch')\n","\n","# Set the y-axis label\n","ax.set_ylabel('Loss')\n","\n","# Add a title\n","ax.set_title('Loss Model 0')\n","\n","# Add a legend\n","ax.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"X1qwzIp4HpfI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Approach 1: DNN to solve regression problem\n","def inverse_mapping_fcn1():\n","\n","  #Define Model and apply dropout and L2 regularisation to prevent overfitting\n","  #Use non linear activation (tanh) for non linear regression\n","  model = tf.keras.Sequential()\n","\n","  model.add(tf.keras.layers.Dense(32, input_shape=(3,), activation='relu', kernel_regularizer = regularizers.l2(0.01)))\n","  model.add(tf.keras.layers.Dropout(0.2))\n","  model.add(tf.keras.layers.Dense(64, activation='relu', kernel_regularizer = regularizers.l2(0.01)))\n","  model.add(tf.keras.layers.Dropout(0.2))\n","  model.add(tf.keras.layers.Dense(4, activation='relu', kernel_regularizer = regularizers.l2(0.01)))\n","  model.add(tf.keras.layers.Dense(units=2, activation='tanh'))\n","\n","  #Compile the model\n","  model.compile(loss='mean_squared_error', optimizer= tf.optimizers.Adam(0.001), metrics=['mse'])\n","\n","  # Define the early stopping callback\n","  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='mse', patience=10)\n","\n","  #Train the model for 100 epochs and allow early stopping\n","  history = model.fit(x_train_scaled, y_train_scaled, epochs=100, validation_data=(x_valid_scaled, y_valid_scaled), callbacks=[early_stopping], batch_size=256)\n","\t\n","  return (model, history)"],"metadata":{"id":"giDc1DiE55yK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#call function\n","print(\"################################## Model 1 ##################################\")\n","[model1, history1] = inverse_mapping_fcn1()\n","\n","# Get the training and validation loss from the model's history\n","val_loss = history1.history['val_loss']\n","\n","# Create a figure and axes\n","fig, ax1 = plt.subplots()\n","\n","# Plot the training and validation loss\n","ax1.plot(val_loss, label='Validation Loss')\n","\n","# Set the x-axis label\n","ax1.set_xlabel('Epoch')\n","\n","# Set the y-axis label\n","ax1.set_ylabel('Loss')\n","\n","# Add a title\n","ax1.set_title('Loss Model 1')\n","\n","# Add a legend\n","ax1.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"rmnH26UTH97m"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Approach 2: Simple MLP to solve regression problem\n","def inverse_mapping_fcn2():\n","\n","  model = MLPRegressor(\n","\t\t\thidden_layer_sizes = (50,2),\n","      max_iter = 300,\n","      solver = \"adam\",\n","\t\t\tactivation = \"logistic\",\n","\t\t\tverbose = True,\n","\t\t\twarm_start = True,\n","\t\t\tearly_stopping = True)\n","  \n","  history = model.fit(x_train_scaled,y_train_scaled) #splits data internally\n","\n","  return (model, history)"],"metadata":{"id":"q6ggnYiGe0zt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#call function\n","print(\"################################## Model 2 ##################################\")\n","[model2, history2] = inverse_mapping_fcn2()\n","\n","# Create figure and subplots\n","fig, ax2 = plt.subplots()\n","\n","# Plot loss\n","ax2.plot(history2.loss_curve_, label='Validation Loss')\n","ax2.set_title('Loss Model 2')\n","ax2.set_xlabel('Epoch')\n","ax2.set_ylabel('Loss')\n","ax2.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"L0OYJiQ0ILo8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Approach 3: Deep MLP to solve regression problem\n","def inverse_mapping_fcn3():\n","\n","  model = MLPRegressor(\n","\t\t\thidden_layer_sizes = (60,3),\n","      max_iter = 300,\n","      solver = \"adam\",\n","\t\t\tactivation = \"logistic\",\n","\t\t\tverbose = True,\n","\t\t\twarm_start = True,\n","\t\t\tearly_stopping = True)\n","  \n","  history = model.fit(x_train_scaled, y_train_scaled)\n","\n","  return (model, history)"],"metadata":{"id":"bb3nEjXoZygA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#call function\n","print(\"################################## Model 3 ##################################\")\n","[model3, history3] = inverse_mapping_fcn3()\n","\n","# Create figure and subplots\n","fig, ax3 = plt.subplots()\n","\n","# Plot loss\n","ax3.plot(history3.loss_curve_, label='Validation Loss')\n","ax3.set_title('Loss Model 3')\n","ax3.set_xlabel('Epoch')\n","ax3.set_ylabel('Loss')\n","ax3.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"j2Efo3hXIQPO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Approach 4: RNN (LSTM) to solve regression problem\n","\n","def inverse_mapping_fcn4():\n","\n","  x_train_lstm = x_train_scaled.reshape(x_train_scaled.shape[0], 1, x_train_scaled.shape[1])\n","  x_valid_lstm = x_valid_scaled.reshape(x_valid_scaled.shape[0], 1, x_valid_scaled.shape[1])\n","\n","  # Define the model architecture\n","  model = tf.keras.Sequential()\n","  model.add(tf.keras.layers.LSTM(32, input_shape=(1,3), return_sequences=True))\n","  model.add(tf.keras.layers.LSTM(4, return_sequences=False))\n","  model.add(tf.keras.layers.Dense(2, activation='tanh'))\n","\n","  # Define the early stopping callback\n","  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='mse', patience=10)\n","\n","  #compile model\n","  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mean_squared_error', metrics=['mse'])\n","\n","  #Train the model for 100 epochs and allow early stopping\n","  history = model.fit(x_train_lstm, y_train_scaled, epochs=100, validation_data=(x_valid_lstm, y_valid_scaled), callbacks=[early_stopping], batch_size=256)\n","\t\n","  return (model, history)"],"metadata":{"id":"9nBWjy848pmi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#call function\n","print(\"################################## Model 4 ##################################\")\n","[model4, history4] = inverse_mapping_fcn4()\n","\n","# Create figure and subplots\n","fig, ax4 = plt.subplots()\n","\n","# Plot loss\n","ax4.plot(history4.history['val_loss'], label='Validation Loss')\n","ax4.set_title('Loss')\n","ax4.set_title('Loss Model 4')\n","ax4.set_xlabel('Epoch')\n","ax4.set_ylabel('Loss')\n","ax4.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"fbHuS1tG-Gri"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Approach 5: RNN to learn correlation between samples. This completly new approach is supposed to learn how to connect multiple samples simultaniously.\n","\n","def inverse_mapping_fcn5(seq_len):\n","\n","  # Get the number of sequences\n","  num_sequences_train = x_train_scaled.shape[0] // seq_len\n","  num_sequences_valid = x_valid_scaled.shape[0] // seq_len\n","\n","  # Reshape x_train_scaled\n","  x_train_lstm = x_train_scaled[:num_sequences_train * seq_len, :]\n","  x_train_lstm = x_train_lstm.reshape(-1, seq_len, 3)\n","\n","  # Reshape y_train_scaled\n","  y_train_lstm = y_train_scaled[:num_sequences_train * seq_len, :]\n","  y_train_lstm = y_train_lstm.reshape(-1, seq_len, 2)\n","\n","  # Reshape x_valid_scaled\n","  x_valid_lstm = x_valid_scaled[:num_sequences_valid * seq_len, :]\n","  x_valid_lstm = x_valid_lstm.reshape(-1, seq_len, 3)\n","\n","  # Reshape x_valid_scaled\n","  y_valid_lstm = y_valid_scaled[:num_sequences_valid * seq_len, :]\n","  y_valid_lstm = y_valid_lstm.reshape(-1, seq_len, 2)\n","\n","  print(\"Shape of x_train_lstm: \", x_train_lstm.shape)\n","  print(\"Shape of y_train_lstm: \", y_train_lstm.shape)\n","  print(\"Shape of x_valid_lstm: \", x_valid_lstm.shape)\n","  print(\"Shape of y_valid_lstm: \", y_valid_lstm.shape)\n","\n","  # Define the model architecture\n","  model = tf.keras.Sequential()\n","  model.add(tf.keras.layers.LSTM(64, input_shape=(seq_len, x_train_scaled.shape[1]), return_sequences=True))\n","  model.add(tf.keras.layers.LSTM(32, return_sequences=True))\n","  model.add(tf.keras.layers.LSTM(8, return_sequences=True))\n","  model.add(tf.keras.layers.Dense(2, activation='tanh'))\n","\n","  # Define the early stopping callback\n","  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='mse', patience=10)\n","\n","  #compile model\n","  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mean_squared_error', metrics=['mse'])\n","\n","  #Train the model for 100 epochs and allow early stopping\n","  history = model.fit(x_train_lstm, y_train_lstm, epochs=100, validation_data=(x_valid_lstm, y_valid_lstm), callbacks=[early_stopping], batch_size=256)\n","\t\n","  return (model, history)"],"metadata":{"id":"gfOOU7RHaO1J"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#call function\n","print(\"################################## Model 5 ##################################\")\n","[model5, history5] = inverse_mapping_fcn5(10)\n","\n","# Create figure and subplots\n","fig, ax5 = plt.subplots()\n","\n","# Plot loss\n","ax5.plot(history5.history['val_loss'], label='Validation Loss')\n","ax5.set_title('Loss')\n","ax5.set_title('Loss Model 5')\n","ax5.set_xlabel('Epoch')\n","ax5.set_ylabel('Loss')\n","ax5.legend()\n","\n","# Show the plot\n","plt.show()"],"metadata":{"id":"KcsMRuOPegvm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Comparing predictions for specific example (line 42) to show how to use ANN to get Motor Activation Values\n","input = np.expand_dims(kinematics[42], axis=0)\n","expected_output = activations[42]\n","\n","#print(df.loc[13])\n","#print(\"Input: \", input)\n","#print(\"Expected: \", expected_output)\n","\n","#model0\n","#predicted_output_0 = scalerOut.inverse_transform(model0.predict(input))\n","#print(\"Prediction 0: \", predicted_output_0)\n","\n","#model1\n","#predicted_output_1 = scalerOut.inverse_transform(model1.predict(input))\n","#print(\"Prediction 1: \", predicted_output_1)\n","\n","#model2\n","#predicted_output_2 = scalerOut.inverse_transform(model2.predict(input))\n","#print(\"Prediction 2: \", predicted_output_2)\n","\n","#model3\n","#prediction3 = scalerOut.inverse_transform(model3.predict(input))\n","#print(\"Prediction 3: \", prediction3)\n","\n","#model4\n","#input_lstm = input.reshape(input.shape[0], 1, input.shape[1])\n","#prediction4 = scalerOut.inverse_transform(model4.predict(input_lstm))\n","#print(\"Prediction 4: \", prediction4)\n","\n","#model5\n","input_lstm = input.reshape(input.shape[0], 1, input.shape[1])\n","prediction5 = scalerOut.inverse_transform(model5.predict(input_lstm))\n","print(\"Prediction 5: \", prediction5)"],"metadata":{"id":"vP3QeijTcOMG"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Motor Babbling**"],"metadata":{"id":"I-ffRZ0UW1eE"}},{"cell_type":"code","source":["### Motor Babbling for NTC Test Bench ###\n","\n","import sys\n","sys.path.append(\"../catkin_ws/devel/lib/python3/dist-packages\")\n","\n","import rospy\n","from bench.msg import BenchState, BenchMotorControl, BenchRecorderControl\n","import csv\n","import time\n","import random\n","import numpy as np\n","\n","def babbling_fcn(babbling_seconds=300, kinematics_activations_show=True):\n","\n","\t#local variables\n","\ttimestep=0.01\n","\trun_samples=int(np.round(babbling_seconds/timestep))\n","\tpass_chance = timestep\n","\tskip_rows = 200\n","\n","\t#TODO: Normalizing current values between 0,1. Might have to change that so that data is normalized in ANN implementation\n","\tmax_in = 1\n","\tmin_in = 0\n","\n","\t#Generating random activations in range (min_in, max_in)\n","\tmotor1_act = systemID_input_gen_fcn(babbling_seconds, pass_chance, max_in, min_in, timestep)\n","\tmotor2_act = systemID_input_gen_fcn(babbling_seconds, pass_chance, max_in, min_in, timestep)\n"," \t\n","\tbabbling_activations = np.transpose(np.concatenate([[motor1_act],[motor2_act]], axis=0))\n"," \n","\t#Save resulting kinematics data\n","\tif kinematics_activations_show:\n","\t\tkinematics_activations_show_fcn(activations=babbling_activations)\n","\t[babbling_kinematics, babbling_activations, chassis_pos] = run_activations_fcn(babbling_activations, timestep)\n"," \n","\t#return kinematics,activations map and skip skip_rows rows (setup phase)\n","\treturn babbling_kinematics[skip_rows:,:], babbling_activations[skip_rows:,:]\n","\n","def systemID_input_gen_fcn(signal_duration_in_seconds, pass_chance, max_in, min_in, timestep):\n","\n","\tnumber_of_samples = int(np.round(signal_duration_in_seconds/timestep))\n","\tsamples = np.linspace(0, signal_duration_in_seconds, number_of_samples)\n"," \n","\tgen_input = np.zeros(number_of_samples,) * min_in\n","\n","\t#generating number_of_samples random activations\n","\tfor i in range(1, number_of_samples):\n","\t\tpass_rand = np.random.uniform(0,1,1)\n","\t\n","\t\tif pass_rand < pass_chance:\n","\t\t\tgen_input[i] = ((max_in-min_in)*np.random.uniform(0,1,1)) + min_in\n","\t\telse:\n","\t\t\tgen_input[i] = gen_input[i-1]\n","\n","\treturn gen_input\n","\n","\n","def kinematics_activations_show_fcn(vs_time=False, timestep=0.01, **kwargs):\n","\n","\t#plotting the resulting kinematics or activations\n","\tsample_no_kinematics=0\n","\tsample_no_activations=0\n","\n","\t#setting up variables\n","\tif (\"kinematics\" in kwargs):\n","\t\tkinematics = kwargs[\"kinematics\"]\n","\t\tsample_no_kinematics = kinematics.shape[0]\n","\n","\tif (\"activations\" in kwargs):\n","\t\tactivations = kwargs[\"activations\"]\n","\t\tsample_no_activations = activations.shape[0]\n","\n","\tif not ((\"kinematics\" in kwargs) or (\"activations\" in kwargs)):\n","\t\traise NameError('Either kinematics or activations needs to be provided')\n","\t\n","\tif (sample_no_kinematics!=0) & (sample_no_activations!=0) & (sample_no_kinematics!=sample_no_activations):\n","\t\traise ValueError('Number of samples for both kinematics and activation matrices should be equal and not zero')\n","\t\n","\telse:\n","\t\tnumber_of_samples = np.max([sample_no_kinematics, sample_no_activations])\n","\t\tif vs_time:\n","\t\t\tx = np.linspace(0,timestep*number_of_samples,number_of_samples)\n","\t\telse:\n","\t\t\tx = range(number_of_samples)\n","\t \n","\t#plotting kinematics: angle degree, angular velocity, angular acceleration\n","\tif (\"kinematics\" in kwargs):\n","\t\tplt.figure()\n","\t\n","\t\tplt.subplot(3, 1, 1)\n","\t\tplt.plot(x, kinematics[:,0])\n","\t\tplt.ylabel('q0 (rads)')\n","\t\n","\t\tplt.subplot(3, 1, 2)\n","\t\tplt.plot(x, kinematics[:,1])\n","\t\tplt.ylabel('q0 dot (rads/s)')\n","\t\n","\t\tplt.subplot(3, 1, 3)\n","\t\tplt.plot(x, kinematics[:,2])\n","\t\tplt.ylabel('q0 double dot (rads/s^2)')\n","\t\n","\t\tplt.xlabel('motor 1 activation values')\n","\t\n","\t#plotting activation values\n","\tif (\"activations\" in kwargs):\n","\t\tplt.figure()\n","\t\n","\t\tplt.subplot(2, 1, 1)\n","\t\tplt.plot(x, activations[:,0])\n","\t\tplt.ylabel('motor 1 activation values')\n","\t\n","\t\tplt.subplot(2, 1, 2)\n","\t\tplt.plot(x, activations[:,1])\n","\t\tplt.ylabel('motor 2 activation values')\n","\n","\tplt.show(block=True)\n"," \n","def run_activations_fcn(activations, timestep):\n","#todo"],"metadata":{"id":"rZ-rdW-cW7Dc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Reinforcement Learning to refine Inverse Kineatics**"],"metadata":{"id":"RKMt1nBByjv7"}},{"cell_type":"code","source":["#Reinforement Learning for particular Learning to refine inverse mapping\n","#model: ANN trained during Motor Babbling\n","#cum_kinematics: kinematics as part of the cummulative training_set collected during Motor Babbling and RL -> Refine ANN\n","#cum_activations: activations as part of the cummulative training_set collected during Motor Babbling and RL -> Refine ANN\n","#penalty_thresh: predefined penalty_threshold to decide which Policy should be choosen\n","#energy_cost_weight: predefined weight for calculating activation values during execution in experiment\n","#refinement: True when ANN should be refined\n","\n","\n"," def learn_to_move_2_fcn(model, cum_kinematics, cum_activations, penalty_thresh=42, energy_cost_weight=0, refinement = False): #need to set default for penalty_thresh\n","\t\n","\t#initializing local variables\n","\texploration_limit = 100\n","\texploitation_limit = 15\n","\tprev_penalty = np.array([0])\n","\tlowest_penalty_so_far = np.array([0])\n","\tbest_model= model\n","\tall_penaltys = []\n","\texploration_run_no = 0\n","\texploitation_run_no = 0\n","\tnew_features = gen_features_fcn(penalty_thresh=penalty_thresh, lowest_penalty_so_far=lowest_penalty_so_far, feat_vec_length=10) #might have to change feat_vec_length\n","\tbest_features_so_far = new_features\n","\texploration_limit_reached = False\n","\n","\t#Check necessaty condition for both exploration phase and exploitation pahse\n","\twhile exploitation_run_no <= exploitation_limit and not exploration_limit_reached:\n","\n","\t\t#count iterations for both exploration and exploitation phase\n","\t\tif lowest_penalty_so_far < penalty_thresh:\n","\t\t\texploration_run_no += 1\n","\t\telse:\n","\t\t\texploitation_run_no += 1\n","\n","\t\t#generate new feature vector F_k. Depending on lowest_penalty_so_far the function gen_features_fcn will choose the correct Policy\n","\t\tnew_features = gen_features_fcn(penalty_thresh=penalty_thresh, lowest_penalty_so_far=lowest_penalty_so_far, prev_penalty=prev_penalty, best_features_so_far=best_features_so_far)\n","\n","\t\t#Execute generated kinematics to interact with environment and receive estimations and real data as well as the penalty\n","\t\t[prev_penalty, est_attempt_kinematics, est_attempt_activations, real_attempt_kinematics, real_attempt_activations] = feat_to_run_attempt_fcn(features=new_features, model=model, energy_cost_weight=energy_cost_weight, feat_show=False)\n","\t\t\n","\t\t#concatinate data to train model and refine\n","\t\t[cum_kinematics, cum_activations] = concatinate_data_fcn(cum_kinematics, cum_activations, real_attempt_kinematics, real_attempt_activations, throw_percentage = 0.20)\n","\t\n","\t\t#Append new penalty\n","\t\tall_penaltys = np.append(all_penaltys, prev_penalty)\n","\n","\t\tif prev_penalty > lowest_penalty_so_far:\n","\t\t\tlowest_penalty_so_far = prev_penalty\n","\t\t\tbest_features_so_far = new_features\n","\t\t\tbest_model = copy_model_fcn(model)\n","\n","\t\tif refinement:\n","\t\t\tmodel = inverse_mapping_fcn1(cum_kinematics, cum_activations, prior_model=model)\n","\n","\t\tprint(\"best penalty so far: \", lowest_penalty_so_far)\n","\t\tif exploration_run_no == exploration_limit and lowest_penalty_so_far < penalty_thresh:\n","\t\t\texploration_limit_reached = True\n","\n","\t#update after run\n","\t[prev_penalty_best, attempt_kinematics_best, est_attempt_activations_best, real_attempt_kinematics_best, real_attempt_activations_best]= \\\n","\tfeat_to_run_attempt_fcn(features = best_features_so_far, model=best_model, energy_cost_weight=energy_cost_weight, feat_show=False)\n","\n","\tprint(\"all_penalty: \", all_penaltys)\n","\tprint(\"prev_penalty_best: \", prev_penalty_best)\n","\n","\treturn lowest_penalty_so_far, all_penaltys, best_features_so_far, real_attempt_activations, exploration_run_no"],"metadata":{"id":"geIVrnnH84ZE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Policy1 + Policy2\n","def gen_features_fcn(penalty_thresh, lowest_penalty_so_far, **kwargs):\n","\n","\t#local variables to generate new vectors\n","\tfeat_min = 0.4\n","\tfeat_max = 0.9\n","\n","\tif (\"best_features_so_far\" in kwargs):\n","\t\tbest_features_so_far = kwargs[\"best_features_so_far\"]\n","\telif (\"feat_vec_length\" in kwargs):\n","\t\tbest_features_so_far = np.random.uniform(feat_min,feat_max,kwargs[\"feat_vec_length\"])\n","\telse:\n","\t\traise NameError('Either best_features_so_far or feat_vec_length needs to be provided')\n","\t\n","\tif lowest_penalty_so_far < penalty_thresh:\n","\t\t#Policy 1: Generate random kinematics data\n","\t\tnew_features = np.random.uniform(feat_min, feat_max, best_features_so_far.shape[0])\n","\telse:\n","\t\t#Policy 2: Use Multivariate Gaussian Distribution Based Stochastic Search\n","\t\tsigma= np.max([(2*penalty_thresh - lowest_penalty_so_far)/100, 0.01])# should be inversly proportional to penalty\n","\t\tnew_features = np.zeros(best_features_so_far.shape[0],)\n","\t\n","\t\tfor i in range(0,len(new_features)):\n","\t\t\tnew_features[i] = np.random.normal(best_features_so_far[i], sigma)\n","\t \n","\t \t#Building new F_k using the new_features from above\n","\t\tnew_features = np.maximum(new_features, feat_min * np.ones(best_features_so_far.shape[0],))\n","\t\tnew_features = np.minimum(new_features, feat_max * np.ones(best_features_so_far.shape[0],))\n","\t\n","\treturn new_features"],"metadata":{"id":"kAePMSsDy1vw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Execute kinematics to interact with environment\n","def feat_to_run_attempt_fcn(features, model,energy_cost_weight=0, feat_show=False):\n","\t[q0_filtered, q1_filtered] = feat_to_positions_fcn(features, show=feat_show)\n"," \n","\tstep_kinematics = positions_to_kinematics_fcn(q0_filtered, q1_filtered, timestep = 0.01)\n"," \n","\tattempt_kinematics = step_to_attempt_kinematics_fcn(step_kinematics=step_kinematics)\n"," \n","\test_attempt_activations = estimate_activations_fcn(model=model, desired_kinematics=attempt_kinematics)\n"," \n"," \t#not implemented yet. Returns Hardcoded values\n","\t[real_attempt_kinematics, real_attempt_activations, chassis_pos]=run_activations_fcn(MuJoCo_model_name=MuJoCo_model_name, est_activations=est_attempt_activations, Mj_render=Mj_render)\n","\n","\treal_attempt_energy = np.square(real_attempt_activations).sum(0).sum(0)\n"," \n","\tprev_penalty = chassis_pos[-1] - energy_cost_weight*real_attempt_energy\n","\n","\treturn prev_penalty, attempt_kinematics, est_attempt_activations, real_attempt_kinematics, real_attempt_activations"],"metadata":{"id":"LVqXNC-KzAGy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def step_to_attempt_kinematics_fcn(step_kinematics, number_of_steps_in_an_attempt = 10):\n","\tattempt_kinematics=np.matlib.repmat(step_kinematics,number_of_steps_in_an_attempt,1)\n","\treturn(attempt_kinematics)"],"metadata":{"id":"Uh_MpRE5zKkk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Merges new data to the current dataset after throwing out the first few samples (defined by throw_percentage)\n","def concatinate_data_fcn( cum_kinematics, cum_activations, kinematics, activations, throw_percentage = 0.20):\n","\tsize_of_incoming_data = kinematics.shape[0]\n","\tsamples_to_throw = int(np.round(throw_percentage * size_of_incoming_data))\n"," \n","\t#concatenate after skipping the firs #samples_to_throw rows\n","\tcum_kinematics = np.concatenate([cum_kinematics, kinematics[samples_to_throw:,:]])\n","\tcum_activations = np.concatenate([cum_activations, activations[samples_to_throw:,:]])\n"," \n","\treturn cum_kinematics, cum_activations"],"metadata":{"id":"hvfvInazzYNO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def copy_model_fcn(original_model):\n","\tconfig=original_model.get_config()\n","\tnew_model=tf.keras.Sequential.from_config(config)\n","\tnew_model.set_weights(original_model.get_weights())\n","\tnew_model.compile(optimizer=tf.train.AdamOptimizer(0.01),loss='mse',metrics=['mse'])  # mean squared error\n","\treturn new_model"],"metadata":{"id":"_CyVqsqNziu4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#calculate \n","def feat_to_positions_fcn(features, timestep=0.01, cycle_duration_in_seconds = 1.3, show=False):\n","\tnumber_of_features = features.shape[0]\n","\teach_feature_length =  int(np.round((cycle_duration_in_seconds/number_of_features)/timestep))\n","\tfeat_angles = np.linspace(0, 2*np.pi*(number_of_features/(number_of_features+1)), number_of_features)\n","\tq0_raw = features*np.sin(feat_angles)\n","\tq1_raw = features*np.cos(feat_angles)\n","\tq0_scaled = (q0_raw*np.pi/3)\n","\tq1_scaled = -1*((-1*q1_raw+1)/2)*(np.pi/2) # since the mujoco model goes from 0 to -pi/2\n","\tq0_scaled_extended = np.append(q0_scaled, q0_scaled[0])\n","\tq1_scaled_extended = np.append(q1_scaled, q1_scaled[0])\n","\n","\tq0_scaled_extended_long = np.array([])\n","\tq1_scaled_extended_long = np.array([])\n","\tfor ii in range(features.shape[0]):\n","\t\tq0_scaled_extended_long = np.append(\n","\t\t\tq0_scaled_extended_long, np.linspace(\n","\t\t\t\tq0_scaled_extended[ii], q0_scaled_extended[ii+1], each_feature_length))\n","\t\tq1_scaled_extended_long = np.append(\n","\t\t\tq1_scaled_extended_long, np.linspace(\n","\t\t\t\tq1_scaled_extended[ii], q1_scaled_extended[ii+1], each_feature_length))\n","\tq0_scaled_extended_long_3 = np.concatenate(\n","\t\t[q0_scaled_extended_long[:-1], q0_scaled_extended_long[:-1], q0_scaled_extended_long])\n","\tq1_scaled_extended_long_3 = np.concatenate(\n","\t\t[q1_scaled_extended_long[:-1], q1_scaled_extended_long[:-1], q1_scaled_extended_long])\n","\n","\tfir_filter_length = int(np.round(each_feature_length/(1)))\n","\tb=np.ones(fir_filter_length,)/fir_filter_length # a simple moving average filter > users can \n","\t#change these if they need smoother pattern\n","\ta=1\n","\tq0_filtered_3 = signal.filtfilt(b, a, q0_scaled_extended_long_3)\n","\tq1_filtered_3 = signal.filtfilt(b, a, q1_scaled_extended_long_3)\n","\n","\tq0_filtered = q0_filtered_3[q0_scaled_extended_long.shape[0]:2*q0_scaled_extended_long.shape[0]-1] # length = 1999 (the \n","\t#very last was ommited since it is going to be the first one on the next cycle)\n","\tq1_filtered = q1_filtered_3[q1_scaled_extended_long.shape[0]:2*q1_scaled_extended_long.shape[0]-1]\n","\tif show:\n","\t\tplt.figure()\n","\t\tplt.scatter(q0_scaled, q1_scaled)\n","\t\tplt.plot(q0_filtered, q1_filtered)\n","\t\tplt.xlabel(\"q0\")\n","\t\tplt.ylabel(\"q1\")\n","\t\tplt.show(block=True)\n","\treturn q0_filtered, q1_filtered"],"metadata":{"id":"qwQdyVihzr05"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def positions_to_kinematics_fcn(q0, q1, timestep = 0.01):\n","\tkinematics=np.transpose(\n","\tnp.concatenate(\n","\t\t(\n","\t\t\t[[q0],\n","\t\t\t[np.gradient(q0)/timestep],\n","\t\t\t[np.gradient(np.gradient(q0)/timestep)/timestep],\n","\t\t\t[q1],\n","\t\t\t[np.gradient(q1)/timestep],\n","\t\t\t[np.gradient(np.gradient(q1)/timestep)/timestep]]),\n","\t\taxis=0\n","\t\t)\n","\t)\n","\treturn kinematics"],"metadata":{"id":"NFuzekSjzyV1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def estimate_activations_fcn(model, desired_kinematics):\n","\n","\tprint(\"running the model\")\n"," \n","\test_activations = model.predict(desired_kinematics)\n","\n","\treturn est_activations"],"metadata":{"id":"7JCUcceaz442"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# results from Hardware/Simulation. Returns hardcoded values at the moment\n","def run_activations_fcn(est_activations, timestep=0.01):\n","\treal_attempt_kinematics = [1543, 119.78934140629462, 597.8981090205831]\n","\treal_attempt_activations = [2.7492387, 0.87028396]\n","\tchassis_pos = [0,0]\n","\treturn real_attempt_kinematics, real_attempt_activations, chassis_pos"],"metadata":{"id":"3popyNKL0AYA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Main**"],"metadata":{"id":"z3CY20seFUFT"}},{"cell_type":"code","source":["# next is to add accel and see the difference\n","# add stiffness too\n","import tensorflow as tf\n","import numpy as np\n","from matplotlib import pyplot as plt\n","import pickle\n","from warnings import simplefilter\n","\n","simplefilter(action='ignore', category=FutureWarning)\n","\n","experiment_ID = \"rl_transfer_learning_1\"\n","\n","mc_run_number = 1\n","babbling_time = 3\n","number_of_refinements = 0\n","errors_all_A_A = np.zeros([2, number_of_refinements+1, mc_run_number])\n","errors_all_A_B = np.zeros([2, number_of_refinements+1, mc_run_number])\n","errors_all_B_B = np.zeros([2, number_of_refinements+1, mc_run_number])\n","\n","stiffness_version_A = 8\n","MuJoCo_model_name_A=\"nmi_leg_w_chassis_air_v{}.xml\".format(stiffness_version_A)\n","MuJoCo_model_name_A_walk=\"nmi_leg_w_chassis_air_v{}_walk.xml\".format(stiffness_version_A)\n","stiffness_version_B = 3\n","MuJoCo_model_name_B=\"nmi_leg_w_chassis_air_v{}.xml\".format(stiffness_version_B)\n","MuJoCo_model_name_B_walk=\"nmi_leg_w_chassis_air_v{}_walk.xml\".format(stiffness_version_B)\n","\n","random_seed = -1\n","\n","for mc_counter in range(mc_run_number):\n","\trandom_seed+=1\n","\t# train model_A\n","\tnp.random.seed(random_seed) # change the seed for different initial conditions\n","\ttf.random.set_random_seed(random_seed)\n","\t[babbling_kinematics, babbling_activations] =\\\n","\t\tbabbling_fcn(\n","\t\t\tMuJoCo_model_name=MuJoCo_model_name_A,\n","\t\t\tsimulation_minutes=babbling_time,\n","\t\t\tkinematics_activations_show=False)\n","\tmodel_A_babble = inverse_mapping_fcn2(\n","\t\tkinematics=babbling_kinematics,\n","\t\tactivations=babbling_activations,\n","\t\tlog_address=\"./logs/{}/scalars/babble_A_mc_run{}/\".format(experiment_ID, mc_counter),\n","\t\tearly_stopping=False)\n","\tcum_kinematics_A_babble = babbling_kinematics\n","\tcum_activations_A_babble = babbling_activations\n","\t#A_A test\n","\tnp.random.seed(random_seed) # change the seed for different initial conditions\n","\ttf.random.set_random_seed(random_seed)\n","\tmodel_A_refined_A=tf.keras.models.clone_model(model_A_babble)\n","\tmodel_A_refined_A.compile(optimizer=tf.train.AdamOptimizer(0.01),\n","          loss='mse',       # mean squared error\n","          metrics=['mse'])  # mean squared error\n","\t[model_A_refined_A, errors, cum_kinematics_A_refined, cum_activations_A_refined] =\\\n","\t\tin_air_adaptation_fcn(\n","\t\t\tMuJoCo_model_name=MuJoCo_model_name_A,\n","\t\t\tmodel=model_A_refined_A,\n","\t\t\tbabbling_kinematics=cum_kinematics_A_babble,\n","\t\t\tbabbling_activations=cum_activations_A_babble,\n","\t\t\tnumber_of_refinements=number_of_refinements,\n","\t\t\tlog_address=\"./logs/{}/scalars/refine_A_A_mc_run{}/\".format(experiment_ID, mc_counter),\n","\t\t\terror_plots_show=False,\n","\t\t\tMj_render=False)\n","\t#import pdb; pdb.set_trace()\n","\terrors_all_A_A[:,:, mc_counter] = [errors[0],errors[1]]\n","\t#A_B test\n","\tnp.random.seed(random_seed) # change the seed for different initial conditions\n","\ttf.random.set_random_seed(random_seed)\n","\tmodel_A_refined_B=tf.keras.models.clone_model(model_A_babble)\n","\tmodel_A_refined_B.compile(optimizer=tf.train.AdamOptimizer(0.01),\n","      loss='mse',       # mean squared error\n","      metrics=['mse'])  # mean squared error\n","\t[model_A_refined_B, errors, cum_kinematics_B_refined, cum_activations_B_refined] =\\\n","\t\tin_air_adaptation_fcn(\n","\t\t\tMuJoCo_model_name=MuJoCo_model_name_B,\n","\t\t\tmodel=model_A_refined_B,\n","\t\t\tbabbling_kinematics=cum_kinematics_A_babble,\n","\t\t\tbabbling_activations=cum_activations_A_babble,\n","\t\t\tnumber_of_refinements=number_of_refinements,\n","\t\t\tlog_address=\"./logs/{}/scalars/refine_A_B_mc_run{}/\".format(experiment_ID, mc_counter),\n","\t\t\terror_plots_show=False,\n","\t\t\tMj_render=False)\n","\terrors_all_A_B[:,:, mc_counter] = [errors[0],errors[1]]\n","## saving the results\n","np.save(\"./results/{}/errors_all_A_A\".format(experiment_ID),errors_all_A_A)\n","np.save(\"./results/{}/errors_all_A_B\".format(experiment_ID),errors_all_A_B)\n","np.save(\"./results/{}/errors_all_B_B\".format(experiment_ID),errors_all_B_B)\n","\n","np.random.seed(random_seed+2) # change the seed for different initial conditions\n","tf.random.set_random_seed(random_seed+2)\n","## Higher level learning (RL)\n","[ lowest_penalty_so_far, all_penaltys, best_features_so_far, real_attempt_activations ]=\\\n"," learn_to_move_fcn(\n"," \tMuJoCo_model_name=MuJoCo_model_name_A_walk,\n"," \tmodel=model_A_refined_A,\n"," \tcum_kinematics=cum_kinematics_A_babble,\n"," \tcum_activations=cum_activations_A_babble,\n"," \tpenalty_thresh=6,\n"," \trefinement=False,\n"," \tMj_render=False)\n","#import pdb; pdb.set_trace()\n","[penaltyA_A, _, _, _, _]=\\\n","feat_to_run_attempt_fcn(MuJoCo_model_name=MuJoCo_model_name_A_walk, features=best_features_so_far, model=model_A_refined_A, feat_show=True, Mj_render=True)\n","[penaltyA_B, _, _, _, _]=\\\n","feat_to_run_attempt_fcn(MuJoCo_model_name=MuJoCo_model_name_B_walk, features=best_features_so_far, model=model_A_refined_A, feat_show=True, Mj_render=False)\n","[penaltyAB_B, _, _, _, _]=\\\n","feat_to_run_attempt_fcn(MuJoCo_model_name=MuJoCo_model_name_B_walk, features=best_features_so_far, model=model_A_refined_B, feat_show=True, Mj_render=False)\n","print(\"penaltyA_A: \", penaltyA_A)\n","print(\"penaltyA_B: \", penaltyA_B)\n","print(\"penaltyAB_B: \", penaltyAB_B)\n","\n","\n","## printing the results\n","# print(\"errors_mean: \",errors_all_A_A.mean(1))\n","# print(\"errors_std: \",errors_all_A_A.std(1))\n","# print(\"errors_mean: \",errors_all_A_B.mean(1))\n","# print(\"errors_std: \",errors_all_A_B.std(1))\n","#import pdb; pdb.set_trace()"],"metadata":{"id":"GiCJl0VAFZxj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"6gcNT1-mWsGu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"nxFRfgz3Z-hf"}}]}
\ No newline at end of file
diff --git a/controllers/g2p-closed-loop/src/functions.py b/controllers/g2p-closed-loop/src/functions.py
new file mode 100644
index 0000000..1d43de5
--- /dev/null
+++ b/controllers/g2p-closed-loop/src/functions.py
@@ -0,0 +1,745 @@
+import numpy as np
+import sklearn
+import rospy
+from numpy import matlib
+from scipy import signal, stats
+from sklearn.neural_network import MLPRegressor
+from sklearn.preprocessing import MinMaxScaler
+from matplotlib import pyplot as plt
+import os
+from copy import deepcopy
+from mpl_toolkits.mplot3d import axes3d
+import matplotlib.lines as mlines
+from copy import deepcopy
+from run import *
+
+def optimize_activations(model, cum_kinematics: np.ndarray, cum_activations: np.ndarray, reward_thresh = 7, refinement = False):
+
+	"""
+	Apply reinforcement learning by generating feature vectors and running the resulting activations on the test bench.
+	These feature vectors contain the activations, kinematics and tendon force for a certain timestep of a particular task.
+	The particular task is defined by a) Point to point movement, b) Sin Wave movement.
+	The reward is defined by the overall tendon tension from both motors and the distance between the current and desired position.
+	The resulting training data which is optimized for activations and tendon force is then used to refine the inverse kinematics model.
+
+	Parameters
+	----------
+		* model: inverse kinematics model
+		* cum_kinematics: kinematics of previous attempts
+		* cum_activations: activations of previous attempts
+		* reward_thresh: threshold for reward (default: 7)
+		* refinement: whether to refine the model after each attempt (default: False)
+
+	Returns
+	-------
+		* model: inverse kinematics model that has been refined in a closed loop fashion
+		* cum_kinematics: kinematics of all attempts
+		* cum_activations: activations of all attempts
+		* all_rewards: list of all rewards
+
+	"""
+	
+	# Reward from previous attempt
+	prev_reward = np.array([0])
+	# Best reward so far
+	best_reward = prev_reward
+	# Model that yielded best reward
+	best_model = model
+	# List of all rewards
+	all_rewards = []
+	# Number of exploitation runs
+	exploitation_count = 0
+	# Generate first feature vector
+	new_features = generate_feature_vector(prev_reward = prev_reward, reward_thresh = reward_thresh, best_reward = best_reward, feat_vec_length = 10)
+	# Save best feature vector
+	best_features = new_features
+
+	# Run 15 exploitation runs.
+	while exploitation_count <= 15:
+
+		# Only increase exploitation count if reward is above threshold
+		if best_reward > reward_thresh:
+			exploitation_count += 1
+
+		# Generate new feature vector
+		new_features = generate_feature_vector(reward_thresh = reward_thresh, best_reward = best_reward, best_features_so_far = best_features)
+
+		[prev_reward, attempt_kinematics, est_attempt_activations, real_attempt_kinematics, real_attempt_activations] = \
+			feat_to_run_attempt_fcn(features=new_features, model=model,feat_show=False, model_ver=1)
+		
+		[cum_kinematics, cum_activations] = \
+		concatinate_training_data(
+			cum_kinematics, cum_activations, real_attempt_kinematics, real_attempt_activations, throw_percentage = 0.20)
+		all_rewards = np.append(all_rewards, prev_reward)
+
+		if prev_reward>best_reward:
+			best_reward = prev_reward
+			best_features = new_features
+			best_model = deepcopy(model)
+
+		if refinement:
+			model = inverse_mapping(cum_kinematics, cum_activations, prior_model=model)
+
+		print("best reward so far: ", best_reward)
+
+	input("Learning to walk completed, Press any key to proceed")
+
+	[prev_reward_best, attempt_kinematics_best, est_attempt_activations_best, real_attempt_kinematics_best, real_attempt_activations_best]= \
+	feat_to_run_attempt_fcn(features=best_features, model=best_model, feat_show=True)
+
+	print("all_reward: ", all_rewards)
+	print("prev_reward_best: ", prev_reward_best)
+	return best_reward, all_rewards
+
+def feat_to_run_attempt_fcn(features, model,feat_show=False,Mj_render=False, model_ver=1):
+	[q0_filtered, q1_filtered] = feat_to_positions_fcn(features, show=feat_show)
+	step_kinematics = positions_to_kinematics_fcn(q0_filtered, q1_filtered, timestep = 0.005)
+	attempt_kinematics = step_to_attempt_kinematics_fcn(step_kinematics=step_kinematics)
+	est_attempt_activations = estimate_activations(model=model, desired_kinematics=attempt_kinematics)
+	[real_attempt_kinematics, real_attempt_activations, chassis_pos]=run_activations(est_attempt_activations, model_ver=model_ver, Mj_render=Mj_render)
+	prev_reward = chassis_pos[-1]
+	return prev_reward, attempt_kinematics, est_attempt_activations, real_attempt_kinematics, real_attempt_activations
+
+def feat_to_positions_fcn(feature_vector: np.ndarray, timestep = 0.005, cycle_duration = 1.3):
+
+	"""
+	Converts a given feature vector to a set of joint angles.
+
+	Parameters
+	----------
+		* feature_vector: vector of features to be converted to joint angles.
+		* timestep: timestep duration in seconds.
+
+
+	"""
+
+
+	number_of_features = feature_vector.shape[0]
+	each_feature_length =  int(np.round((cycle_duration/number_of_features)/timestep))
+	feat_angles = np.linspace(0, 2*np.pi*(number_of_features/(number_of_features+1)), number_of_features)
+	q0_raw = feature_vector*np.sin(feat_angles)
+	q1_raw = feature_vector*np.cos(feat_angles)
+	q0_scaled = (q0_raw*np.pi/3)
+	q1_scaled = -1*((-1*q1_raw+1)/2)*(np.pi/2) # since the mujoco model goes from 0 to -pi/2
+	q0_scaled_extended = np.append(q0_scaled, q0_scaled[0])
+	q1_scaled_extended = np.append(q1_scaled, q1_scaled[0])
+
+	q0_scaled_extended_long = np.array([])
+	q1_scaled_extended_long = np.array([])
+	for ii in range(feature_vector.shape[0]):
+		q0_scaled_extended_long = np.append(
+			q0_scaled_extended_long, np.linspace(
+				q0_scaled_extended[ii], q0_scaled_extended[ii+1], each_feature_length))
+		q1_scaled_extended_long = np.append(
+			q1_scaled_extended_long, np.linspace(
+				q1_scaled_extended[ii], q1_scaled_extended[ii+1], each_feature_length))
+	q0_scaled_extended_long_3 = np.concatenate(
+		[q0_scaled_extended_long[:-1], q0_scaled_extended_long[:-1], q0_scaled_extended_long])
+	q1_scaled_extended_long_3 = np.concatenate(
+		[q1_scaled_extended_long[:-1], q1_scaled_extended_long[:-1], q1_scaled_extended_long])
+
+	fir_filter_length = int(np.round(each_feature_length/(1)))
+	b=np.ones(fir_filter_length,)/fir_filter_length # a simple moving average filter > users can 
+	#change these if they need smoother pattern
+	a=1
+	q0_filtered_3 = signal.filtfilt(b, a, q0_scaled_extended_long_3)
+	q1_filtered_3 = signal.filtfilt(b, a, q1_scaled_extended_long_3)
+
+	q0_filtered = q0_filtered_3[q0_scaled_extended_long.shape[0]:2*q0_scaled_extended_long.shape[0]-1] # length = 1999 (the 
+	#very last was ommited since it is going to be the first one on the next cycle)
+	q1_filtered = q1_filtered_3[q1_scaled_extended_long.shape[0]:2*q1_scaled_extended_long.shape[0]-1]
+	
+	return q0_filtered, q1_filtered
+
+def step_to_attempt_kinematics_fcn(step_kinematics, number_of_steps_in_an_attempt = 10):
+	attempt_kinematics=np.matlib.repmat(step_kinematics,number_of_steps_in_an_attempt,1)
+	return(attempt_kinematics)
+
+def generate_feature_vector(reward_thresh: float, best_reward: float, feat_min = 0.4, feat_max = 0.9, **kwargs):
+
+	"""
+	Generates a feature vector which describes the particular task.
+		a) Policy 1 (Exploration): Generate a random feature vector
+		b) Policy 2 (Exploitation): Generate a feature vector based on Multivariate Gaussian Distribution Based Stochastic Search
+	The feature vector is used to generate the desired kinematics for the attempt.
+
+	Parameters
+	----------
+		* reward_thresh: the reward threshold that needs to be reached to generate a new feature vector
+		* best_reward_so_far: the best reward so far
+		* feat_min: the minimum value for the feature vector values
+		* feat_max: the maximum value for the feature vector values
+		* kwargs: either "best_features_so_far" or "feat_vec_length" needs to be provided
+
+	Returns
+	-------
+		* new_features: the new feature vector
+	"""
+
+	if ("best_features_so_far" in kwargs):
+		#save the best feature vector so far if it is provided to apply Multivariate Gaussian Distribution Based Stochastic Search on it
+		best_features_so_far = kwargs["best_features_so_far"]
+	elif ("feat_vec_length" in kwargs):
+		#generate a random feature vector from uniform distribution if the length is provided
+		best_features_so_far = np.random.uniform(feat_min, feat_max, kwargs["feat_vec_length"])
+	else:
+		raise NameError('Either best_features_so_far or feat_vec_length needs to be provided')
+	
+	#Policy 1 (Exploration Phase): Generate random kinematics data
+	if best_reward < reward_thresh:
+		new_features = np.random.uniform(feat_min, feat_max, best_features_so_far.shape[0])
+
+	#Policy 2 (Exploitation Phase): Use Multivariate Gaussian Distribution Based Stochastic Search
+	else:
+		#standard deviation should be inversly proportional to reward and positive
+		sigma = np.max([(12 - best_reward) / 100, 0.01])
+
+		#new feature vector
+		new_features = np.zeros(best_features_so_far.shape[0],)
+
+		#Iterate over each feature and generate a new feature based on Multivariate Gaussian Distribution Based Stochastic Search
+		for i in range(0, len(new_features)):
+			new_features[i] = np.random.normal(loc = best_features_so_far[i], scale = sigma)
+
+		#Make sure that the new feature vector is within the bounds defined by feat_min and feat_max
+		new_features = np.maximum(new_features, feat_min * np.ones(best_features_so_far.shape[0],))
+		new_features = np.minimum(new_features, feat_max * np.ones(best_features_so_far.shape[0],))
+
+	#return the new feature vector
+	return new_features
+
+def execute_refinement(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, particular_kinematics: np.ndarray, input_kinematics: np.ndarray, input_activations: np.ndarray, P: np.ndarray, I: np.ndarray, num_refinements = 10, timestep = 0.005):
+
+	"""
+	Refines the ANN model by training the model with optimized training data for the particular kinematics.
+	We apply reinforcement learning to optimize for the activations of the training data so that the motors don't fight each other while moving the endeffector to the desired position.
+	The activations are applied to the motors in a closed loop fashion using a PI controller.
+
+	Parameters
+	----------
+		* model: The ANN model to be refined
+		* scalerIn: The scaler used to scale the input data
+		* scalerOut: The scaler used to scale the output data
+		* particular_kinematics: The kinematics of the particular task that is used to optimize the training data
+		* input_kinematics: The kinematics of the input data (current Training Data)
+		* input_activations: The activations of the intput data (current Training Data)
+		* P: The P matrix of the PI controller
+		* I: The I matrix of the PI controller
+		* num_refinements: The number of times the model is refined
+		* timestep: The timestep of the experiment
+		
+	Returns
+	-------
+		* model: The refined model
+		* scalerIn_new: The new scaler used to scale the input data
+		* scalerOut_new: The new scaler used to scale the output data
+		* cum_kinematics: The kinematics of the input data and the particular kinematics
+		* cum_activations: The activations of the input data and the particular kinematics
+		* actual_kinematics: The actual kinematics of the testbench after feedback execution
+		* actual_activations: The actual activations of the testbench after feedback execution
+		* particular_kinematics: The particular kinematics of the experiment
+		* errors: The kinematics errors of the model after each refinement
+		* average_error: The average error of the model with the input data
+	"""
+
+	#Run closed loop execution on testbench to receive the actual kinematics, activations and tendon tensions for RL
+	average_error, actual_kinematics, actual_activations, actual_tendon_forces = feedback_execution(model = model, scalerIn = scalerIn, scalerOut = scalerOut, desired_kinematics = particular_kinematics, P = P, I = I)
+
+	# Errors of the model after each refinement
+	errors = []
+
+	#Cumuative kinematics and activations
+	cum_kinematics = input_kinematics
+	cum_activations = input_activations
+
+	#Refinement loop
+	for i in range(num_refinements):
+		
+		#Concatinate the training data with the data collected during the last refinement
+		cum_kinematics, cum_activations = concatinate_training_data(prev_kinematics = input_kinematics, prev_activations = input_activations, new_kinematics = actual_kinematics, new_activations = actual_activations)
+
+		#Retrain the model with the new training data and save the new MinMaxScalers
+		refined_model, scalerIn_new, scalerOut_new = inverse_mapping(kinematics = cum_kinematics, activations = cum_activations, prior_model = model)
+
+		#Run closed loop execution on testbench to receive the actual kinematics and activations as well as the average error
+		average_error_i, actual_kinematics, actual_activations, actual_tendon_forces = feedback_execution(model = refined_model, scalerIn = scalerIn_new, scalerOut = scalerOut_new, desired_kinematics = particular_kinematics, P = P, I = I, timestep = timestep)
+		
+		#append the average error
+		np.append(errors, average_error_i)
+
+	#calculate the average error of the model with the input data
+	average_error = np.mean(errors)
+
+	return refined_model, scalerIn_new, scalerOut_new, cum_kinematics, cum_activations, actual_kinematics, actual_activations, particular_kinematics, errors, average_error
+
+def concatinate_training_data(prev_kinematics: np.ndarray, prev_activations: np.ndarray, new_kinematics: np.ndarray, new_activations: np.ndarray, throw_percentage = 0.20):
+
+	"""
+	Concatinates data from previous training with new data from refinment phase of g2p
+
+	Parameters
+	----------
+		* prev_kinematics: kinematics from previous training
+		* prev_activations: activations from previous training
+		* new_kinematics: kinematics from refinment phase of g2p
+		* new_activations: activations from refinment phase of g2p
+		* throw_percentage: percentage of data to throw away from the beginning of the new data
+
+	Returns
+	-------
+		* cum_kinematics: cumulated kinematics
+		* cum_activations: cumulated activations
+	"""
+
+	size_of_incoming_data = new_kinematics.shape[0]
+	samples_to_throw = int(np.round(throw_percentage*size_of_incoming_data))
+	prev_kinematics = np.concatenate([prev_kinematics, new_kinematics[samples_to_throw:,:]])
+	prev_activations = np.concatenate([prev_activations, new_activations[samples_to_throw:,:]])
+	return prev_kinematics, prev_activations
+
+def inverse_mapping(kinematics: np.ndarray, activations: np.ndarray, early_stopping = False, **kwargs):
+
+	"""
+	Trains an MLP that maps activations to kinematics (Inverse Mapping).
+	If a prior model is passed, the model will be trained on the new data otherwise a new model will be created and trained on the data.
+	As shown in colab notebook (TODO: add link) "model 2" performs the best which is why it is used here
+
+	Parameters
+	----------
+		* kinematics: kinematics numpy array with shape (n_samples, 3)
+		* activations: activations numpy array with shape (n_samples, 2)
+		* early_stopping: if True, the model will stop training when the validation loss stops decreasing
+		* kwargs: prior_model: if a model is passed, the model will be trained on the new data
+
+	Returns
+	-------
+		* model: the trained model
+		* scalerIn: the scaler used to scale the input data
+		* scalerOut: the scaler used to scale the output data -> use scalerOut.inverse_transform(model.predict(input)) to get the predicted activations
+	"""
+
+	#Scale training data for better performance
+	scalerIn = MinMaxScaler()
+	scalerOut = MinMaxScaler()
+	kinematics_scaled = scalerIn.fit_transform(kinematics)
+	activations_scaled = scalerOut.fit_transform(activations)
+
+	#Keep the model if it is passed as an argument
+	if ("prior_model" in kwargs):
+		model = kwargs["prior_model"]
+	#Create a new model when no model is passed
+	else:
+		model = MLPRegressor(
+			hidden_layer_sizes = (50,2),
+      		max_iter = 300,
+      		solver = "adam",
+			activation = "logistic",
+			verbose = True,
+			warm_start = True,
+			early_stopping = early_stopping)
+
+	#train model
+	model.fit(kinematics_scaled, activations_scaled)
+
+	#return trained model and MinMaxScaler
+	return (model, scalerIn, scalerOut)
+
+def estimate_activations(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, desired_kinematics: np.ndarray):
+
+	"""
+	Estimates the activations from the kinematics using the inverse mapping learned by the model.
+	
+	Parameters
+	----------
+		* model: the model that maps kinematics to activations
+		* scalerIn: the scaler used to scale the input data
+		* scalerOut: the scaler used to scale the output data
+		* desired_kinematics: the desired kinematics 
+
+	Returns
+	-------
+		* estimated_activations: the estimated activations to achieve the desired kinematics
+	"""
+
+	#scale kinematics
+	desired_kinematics_scaled = scalerIn.transform(desired_kinematics)
+	#predict activations
+	estimated_activations_scaled = model.predict(desired_kinematics_scaled)
+	#inverse scaling so that the activations are in the correct range
+	estimated_activations = scalerOut.inverse_transform(estimated_activations_scaled)
+
+	#return estimated activations
+	return estimated_activations
+
+def generate_p2p_kinematics(low: float, high: float, num_positions: int, position_duration: float, timestep: float):
+
+	"""
+	Generating random kinematics for a point to point refinement phase of the g2p task. The angle implies the position of the endeffector.
+
+	Parameters
+	----------
+		* low: lower bound for random angles
+		* high: upper bound for random angles
+		* num_positions: number of positions to generate
+		* position_duration: duration of holding each angle in seconds
+		* timestep: timestep of the test bench
+
+	Returns
+	-------
+		* random_kinematics: random kinematics for the test bench to follow
+	"""
+
+	#calculating number of samples for each position
+	num_samples = position_duration / timestep
+
+	#array for storing random kinematics
+	random_angles = np.zeros(int(np.round(num_positions * num_samples)),)
+
+	#generating random positions
+	for i in range(num_positions):
+		#generating random value
+		random_value = ((high-low) * (np.random.rand(1)[0])) + low
+		#repeat random value for number of samples
+		random_value_sampled = np.repeat(random_value, num_samples)
+		#fill array with random values in the correct interval as described by num_samples
+		random_angles[int(i * num_samples) : int((i+1) * num_samples)] = random_value_sampled
+
+	#calculate kinematics from angles
+	random_kinematics = calculate_kinematics(random_angles, timestep = timestep)
+
+	return random_kinematics
+
+def generate_sin_kinematics(attempt_length: int , num_cycles: int, timestep = 0.005):
+
+	"""
+	Generating continuous kinematics for the refinement phase of the g2p task. The kinematics are generated by a sine wave.
+
+	Parameters
+	----------
+		* attempt_length: length of the attempt in seconds
+		* num_cycles: number of cycles to complete in the attempt
+		* timestep: timestep of the kinematics
+
+	Returns
+	-------
+		* continuous_kinematics: continuous kinematics for the testbench to follow
+	"""
+
+	#calculate the number of samples
+	num_samples = int(np.round(attempt_length / timestep))
+
+	#initialize the kinematics vector
+	q0 = np.zeros(num_samples)
+
+	#loop through the samples and calculate angles
+	for i in range(num_samples):
+		q0[i]=(np.pi/3)*np.sin(num_cycles*(2*np.pi*i/num_samples))
+
+	#calculate the kinematics from angles
+	continuous_kinematics = calculate_kinematics(angles = q0, timestep = timestep)
+
+	#return the kinematics
+	return continuous_kinematics
+
+def calculate_kinematics(angles: np.ndarray, timestep = 0.005):
+
+	"""
+	Converting joint angles to kinematics (angle, velocity, acceleration)
+
+	Parameters
+	----------
+		* angles: angles of joint 0
+		* timestep: time between two samples
+
+	Returns
+	-------
+		* kinematics: kinematics numpy array with shape (n_samples, 3) for joint 0. Each row is a sample and the columns are angle, velocity and acceleration
+	"""
+
+	#calculating velocity and acceleration
+	angular_velocities = np.gradient(angles) / timestep
+	angular_accelerations = np.gradient(angular_velocities) / timestep
+
+	#concatenating kinematics into one array with shape (n_samples, 3) with every row being a sample and the columns being angle, velocity and acceleration
+	kinematics = np.concatenate(([[angles], [angular_velocities], [angular_accelerations]]), axis = 0)
+
+	#transposing kinematics
+	kinematics = np.transpose(kinematics)
+
+	return kinematics
+
+def calculate_kinematics_error(desired_kinematics: np.ndarray, real_kinematics: np.ndarray, error_type = "RMSE"):
+
+    """
+    Calculates the error between desired kinematics and actual kinematics
+    Either mean abs value or RMSE
+
+    Parameters
+    ----------
+        * desired_kinematics: desired kinematics (angle, velocity, acceleration)
+        * real_kinematics: real kinematics (angle, velocity, acceleration)
+        * error_type: type of error to calculate. Either "MAE" or "RMSE"
+
+    Returns
+    -------
+        * error: error vector between the two kinematic vectors or -1 if error type not recognized
+    """
+
+    #local variables
+    num_samples = desired_kinematics.shape[0]
+
+    #initializing error vector for all kinematics sample
+    error = np.zero(num_samples)
+
+    for i in range(num_samples):
+        if(error_type == "MAE"):
+            error[i] = np.mean(np.abs(desired_kinematics[i,:] - real_kinematics[i,:]))
+        elif(error_type == "RMSE"):
+            error[i] = np.sqrt(np.power(desired_kinematics[i,:]-real_kinematics[i,:],2).mean())
+        else:
+            print("Error type not recognized")
+            error = -1
+
+    return error
+
+##### Feedback Functions #####
+
+def feedback_execution(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, desired_kinematics: np.ndarray, P: np.ndarray, I: np.ndarray, delay_timesteps = 0, timestep = 0.005):
+
+	"""
+	Executes desired_kinematics in closed loop with the given model on the testbench
+
+	Parameters
+	----------
+		* model: pre-trained model from motor babbling
+		* scalerIn: scaler for input data
+		* scalerOut: scaler for output data
+		* desired_kinematics: desired kinematics for the joints
+		* P: proportional gain for each joint
+		* I: integral gain for each joint
+		* delay_timesteps: delay between desired and actual kinematics
+		* timestep: timestep of the curent kinematics
+
+	Returns
+	-------
+		* average_kinematics_error: average error of the joint kinematics
+		* actual_kinematics: real kinematics of the joint
+		* actual_activations: real activations of motors [flex, extend]
+		* actual_tendon_forces: real tendon forces of the motor tendons [flex, extend]
+	"""
+
+	#estimate activations for desired kinematics. These will be the initial activations which will be corrected in the feedback loop
+	est_activations = estimate_activations(model = model, scalerIn = scalerIn, scalerOut = scalerOut,  desired_kinematics = desired_kinematics)
+
+	#number of task samples
+	num_samples = desired_kinematics.shape[0]
+	#initialize input kinematics vector
+	input_kinematics = np.zeros(desired_kinematics.shape)
+	#initialize actual angles vector
+	actual_angles = np.zeros([num_samples,1])
+	#initialize actual activations vector
+	actual_activations = np.zeros([num_samples,2])
+	#initialize actual kinematics vector
+	actual_kinematics = np.zeros([num_samples,1])
+	#initialize actual tendon forces vector
+	actual_tendon_forces = np.zeros([num_samples,2])
+	#initialize cumulative error vector
+	q_error_cum = np.zeros([num_samples,1])
+	#defines how many timesteps (samples) we need to look back to calculate the gradient with feedback
+	gradient_edge_order = 1
+
+	#calculate activations based on kinematics with feedback for every sample in a feedback loop
+	for i in range(num_samples):
+
+		#input kinematics = desired_kinematics for sample i when no feedback is available yet at beginning
+		if i < max(gradient_edge_order, delay_timesteps + 1):
+			input_kinematics[i,:] = desired_kinematics[i,:]
+
+		#calculate input kinematics for sample i with respect to feedback of previous sample
+		else:
+			[input_kinematics[i,:], q_error_cum] = calculate_input_kinematics(
+				step_number = i,
+				actual_angles = actual_angles,
+				desired_kinematics = desired_kinematics,
+				q_error_cum = q_error_cum,
+				P = P,
+				I = I,
+				delay_timesteps = delay_timesteps,
+				gradient_edge_order = gradient_edge_order,
+				timestep = timestep)
+
+		#update estimated activations after feedback. The input kinematics has been updated with the feedback
+		est_activations[i,:] = estimate_activations(model = model, scalerIn = scalerIn, scalerOut = scalerOut, desired_kinematics = [input_kinematics[i,:]])[0,:]
+
+		#Execute the estimated activations on the testbench and save the actual kinematics, activations and tendon forces as feedback for the next timestep
+		actual_kinematics[i,:], actual_activations[i,:], actual_tendon_forces[i,:] = run_activations(activations = est_activations[i,:], timestep = timestep)
+
+		#save actual angles for feedback
+		actual_angles[i,:] = actual_kinematics[i,0]
+
+	#calculate error for every joint and average error over all joints (here only one joint)
+	kinematics_errors = calculate_kinematics_error(desired_kinematics[:,0], actual_kinematics[:,0], error_type = "RMSE")
+	average_kinematics_error = np.mean([kinematics_errors])
+
+	return average_kinematics_error, actual_kinematics, actual_activations, actual_tendon_forces
+
+def calculate_input_kinematics(step_number: int, actual_angles: np.ndarray, desired_kinematics: np.ndarray, q_error_cum: np.ndarray, P: np.ndarray, I: np.ndarray, delay_timesteps: int, gradient_edge_order = 1, timestep = 0.005):
+
+    """
+    PI controller like function that calculates K_p * error + K_i * integral(error).
+    This returns the input kinematics for the next timestep
+
+    Parameters
+    ----------
+        * step_number: current timestep
+        * actual_angle: actual angle of the joint at current timestep
+        * desired_kinematics: desired kinematics of the joint at current timestep
+        * q_error_cum: cumulative error of the joint at current timestep
+        * P: proportional gain for each joint
+        * I: integral gain for each joint
+        * delay_timesteps: delay between desired and actual kinematics
+        * gradient_edge_order: defines how many timesteps we need to look back to calculate the gradient with feedback
+        * timestep: duration of each timestep in seconds
+
+    Returns
+    -------
+        * input_kinematics: input kinematics for the next timestep
+        * q_error_cum: cumulative error of the joint
+    """
+
+    #desired kinematics (angle and velocity) stacking together kinematcis of every joint (for now only one joint)
+    q_desired =  desired_kinematics[step_number, np.ix_([0])][0]
+    q_dot_desired = desired_kinematics[step_number, np.ix_([1])][0]
+
+    #calculating errors between desired and actual angles using feedback from previous timestep
+    q_plant = actual_angles[step_number - gradient_edge_order - delay_timesteps, :]
+    q_error = q_desired - q_plant
+    q_error_cum[step_number, :] = q_error
+
+    #Implementing PI controller (K_p * error + K_i * integral(error)) to adapt the input velocity
+    q_dot_in = q_dot_desired + P * q_error + I * (q_error_cum.sum(axis = 0) * timestep)
+
+    #calculating input angle and acceleration
+    q_in = q_desired
+    q_double_dot_in = calculate_kinematics(q_0 = q_in, timestep = timestep)[-1]
+    #q_double_dot_in = [np.gradient(desired_kinematics[step_number - gradient_edge_order:step_number + 1, 1], edge_order = gradient_edge_order)[-1] / timestep]
+
+    input_kinematics = [q_in[0], q_dot_in[0], q_double_dot_in[0]]
+    
+    return input_kinematics, q_error_cum
+
+
+#### Refinement Functions ####
+
+def refine(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, babbling_kinematics: np.ndarray, babbling_activations: np.ndarray, P: np.ndarray, I: np.ndarray, num_refinements = 10, timestep = 0.005):
+
+	"""
+	Refines the ANN model by collecting more training data that is optimized for tendon force so that the motors don't fight each other while achiving predefined kinematic tasks.
+	In this case the task is to move the endeffector precisely according to the RL Experiments:
+		a) P2P: The endeffector is moved to random non consecutive positions
+		b) sin: The endeffector is moved to consecutive positions that are a sin function of time
+	The optimized Training data is then used to refine the model. #TODO: Maybe completly retrain the model with the optimized data instead of refining it. -> Test
+	
+	Please note: The idea to combine both RL experiments is what I came up with. In case it will worsen the performance we can just use one of them -> Probably the P2P experiment.
+	Just recently I realized that we need to optimize the training data itself which is why apply the Reinforcement Learning to the motor activations in order to achive less tendon force and not to the kinematics.
+
+	Parameters
+	----------
+		* model: The ANN model to be refined
+		* scalerIn: The scaler for the input data
+		* scalerOut: The scaler for the output data
+		* babbling_kinematics: The kinematics of the babbling data
+		* babbling_activations: The activations of the babbling data
+		* P: The P matrix of the PI Controller
+		* I: The I matrix of the PI Controller
+		* num_refinements: The number of times the model is refined
+		* timestep: The duration of one timestep in seconds
+		
+	Returns
+	-------
+		* refined_model: The refined model refined after both RL experiments
+		* errors: The kinematics errors of the model after each refinement
+		* cum_kinematics: The kinematics of the babbling data and the data collected during the refinements
+		* cum_activations: The activations of the babbling data and the data collected during the refinements
+	"""
+
+	# Refine the model by optimizing data from the P2P experiment for less tendon force
+	p2p_refined_model, scalerIn_p2p, scalerOut_p2p, errors, cum_kinematics, cum_activations = refine_p2p_task(model = model, scalerIn = scalerIn, scalerOut = scalerOut, input_kinematics = babbling_kinematics, input_activations = babbling_activations, P = P, I = I, num_refinements = num_refinements, timestep = timestep)
+
+	# Refine the p2p refined model by optimizing data from the sin experiment for less tendon force
+	refined_model, scalerIn_sin, scalerOut_sin, errors, cum_kinematics, cum_activations = refine_sin_task(model = p2p_refined_model, scalerIn = scalerIn_p2p, scalerOut = scalerOut_p2p, input_kinematics = cum_kinematics, input_activations = cum_activations, P = P, I = I, num_refinements = num_refinements, timestep = timestep)
+
+	# Return the refined model with the optimized data from both experiments
+	return refined_model, scalerIn_sin, scalerOut_sin, errors, cum_kinematics, cum_activations
+
+def refine_p2p_task(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, input_kinematics: np.ndarray, input_activations: np.ndarray, P: np.ndarray, I: np.ndarray, num_refinements = 10, timestep = 0.005):
+
+	"""
+	Refines the ANN model by collecting optimized training data that is particlar to the task of Point to Point movement before training the model with the newly generated and optimized data.
+	In this case the task is to move the endeffector precisely to random non consecutive positions.
+
+	Parameters
+	----------
+		* model: The ANN model to be refined
+		* input_kinematics: The kinematics of the input data
+		* input_activations: The activations of the intput data
+		* P: The P matrix of the PI controller
+		* I: The I matrix of the PI controller
+		* num_refinements: The number of times the model is refined
+		* timestep: The timestep of the experiment
+		
+	Returns
+	-------
+		* model: The refined model
+		* scalerIn: The scaler for the input data
+		* scalerOut: The scaler for the output data
+		* errors: The kinematics errors of the model after each refinement
+		* cum_kinematics: The kinematics of the input data and the data collected during the refinement
+		* cum_activations: The activations of the input data and the data collected during the refinement
+	"""
+
+	# Generate random non continuous kinematics for point to point experiment
+	particular_kinematics = generate_p2p_kinematics(low = -(np.pi / 3), high = np.pi / 3, num_positions = 10, position_duration = 2.5, timestep = timestep)
+
+	# Execute refinement with the particular kinematics to optimize the training data
+	refined_model, scalerIn_new, scalerOut_new, errors, cum_kinematics, cum_activations = execute_refinement(model = model, scalerIn = scalerIn, scalerOut = scalerOut, particular_kinematics = particular_kinematics, input_kinematics = input_kinematics, input_activations = input_activations, P = P, I = I, num_refinements = num_refinements)
+
+	# Return the refined model with the optimized data from the P2P experiment
+	return refined_model, scalerIn_new, scalerOut_new, errors, cum_kinematics, cum_activations
+
+def refine_sin_task(model, scalerIn: MinMaxScaler, scalerOut: MinMaxScaler, input_kinematics: np.ndarray, input_activations: np.ndarray, P: np.ndarray, I: np.ndarray, num_refinements = 10, timestep = 0.005):
+
+	"""
+	Refines the ANN model by collecting more training data that is particlar to the task of sin movements befor training the model with the newly generated and optimized data.
+	In this case the task is to move the endeffector precisely to consecutive positions that are a sin function of time
+
+	Parameters
+	----------
+		* model: The ANN model to be refined
+		* scalerIn: The scaler used to scale the input data
+		* scalerOut: The scaler used to scale the output data
+		* input_kinematics: The kinematics of the input data
+		* input_activations: The activations of the input data
+		* P: The P matrix of the PI Controller
+		* I: The I matrix of the PI Controller
+		* num_refinements: The number of times the model is refined
+		* timestep: The duration of one timestep in seconds
+		
+	Returns
+	-------
+		* model: The refined model
+		* scalerIn: The scaler for the input data
+		* scalerOut: The scaler for the output data
+		* errors: The kinematics errors of the model after each refinement
+		* cum_kinematics: The kinematics of the input data and the data collected during the refinement
+		* cum_activations: The activations of the input data and the data collected during the refinement
+	"""
+
+	# Generate continuous sin kinematics for sin experiment
+	particular_kinematics = generate_sin_kinematics(attempt_length = 10, num_cycles = 7 )
+
+	# Execute refinement with the particular kinematics to optimize the training data
+	refined_model, scalerIn_new, scalerOut_new, errors, cum_kinematics, cum_activations = execute_refinement(model = model, scalerIn = scalerIn, scalerOut = scalerOut, particular_kinematics = particular_kinematics, input_kinematics = input_kinematics, input_activations = input_activations, P = P, I = I, num_refinements = num_refinements)
+
+	# Return the refined model with the optimized data from the Sin experiment
+	return refined_model, scalerIn_new, scalerOut_new, errors, cum_kinematics, cum_activations
\ No newline at end of file
diff --git a/controllers/g2p-closed-loop/src/main.py b/controllers/g2p-closed-loop/src/main.py
new file mode 100644
index 0000000..f89e1f5
--- /dev/null
+++ b/controllers/g2p-closed-loop/src/main.py
@@ -0,0 +1,50 @@
+import numpy as np
+from matplotlib import pyplot as plt
+from warnings import simplefilter
+from tensorflow.keras.models import save_model
+from functions import *
+from motor_babbling import *
+
+# Make sure we always get the same random data
+np.random.seed(0)
+
+# Generate first training data with motor babbling
+[babbling_kinematics, babbling_activations] = motor_babbling(babbling_seconds = 300, timestep = 0.01, pass_chance = 0.01, skip_rows = 200, max_in = 1, min_in = 0)
+
+# Create training database initially with babbling data
+cum_kinematics = babbling_kinematics
+cum_activations = babbling_activations
+
+# Create inverse mapping with babbling data and save MinMaxScaler from input and output
+model, scalerIn, scalerOut = inverse_mapping(kinematics = babbling_kinematics, activations = babbling_activations, early_stopping = True)
+
+# Constants for PID Controller
+# TODO: Might have to be tuned/adjusted
+# TODO: Should we use D as well? -> Make it more responsive to changes in error
+P = np.array([10])
+I = np.array([2])
+
+# Number of trials to get refined model
+trial_number = 1
+# Average error of predictions for each trial
+average_error = []
+# List of all models from each trial
+models = []
+#Make sure we always get the same random data
+np.random.seed(0)
+
+# Iterate over trials and refine model
+for i in range(trial_number):
+
+    # Refine model 10 times with optimized training data
+    model, errors, cum_kinematics, cum_activations = refine(model = model, scalerIn = scalerIn, scalerOut = scalerOut, babbling_kinematics = babbling_kinematics, babbling_activations = babbling_activations, P = P, I = I, num_refinements = 10, timestep = 0.005)
+
+    # Save model and average error for each trial
+    models.append(model)
+    average_error.append(np.mean(errors))
+
+# Find model with lowest average error
+best_model = models[np.argmin(average_error)]
+
+# Save best model as h5 file. It can be used for inverse mapping!
+save_model(best_model, 'model.h5')
\ No newline at end of file
diff --git a/controllers/g2p-closed-loop/src/motor_babbling.py b/controllers/g2p-closed-loop/src/motor_babbling.py
new file mode 100644
index 0000000..6165c8c
--- /dev/null
+++ b/controllers/g2p-closed-loop/src/motor_babbling.py
@@ -0,0 +1,75 @@
+from functions import *
+import numpy as np
+
+def motor_babbling(babbling_seconds = 300, timestep = 0.01, pass_chance = 0.01, skip_rows = 200, max_in = 1, min_in = 0):
+
+    """
+    Implementation of motor babbling to create an inverse kinematics model of the test bench
+
+    Parameters
+    ----------
+        * babbling_seconds: number of seconds to babble (default: 300)
+        * timestep: duration of each timestep in seconds (default: 0.01)
+        * pass_chance: probability of activation being passed to motor (default: 0.01)
+        * skip_rows: number of rows to skip in babbling data (setup phase) (default: 200)
+        * max_in: maximum activation value (default: 1)
+        * min_in: minimum activation value (default: 0)
+
+    Returns
+    -------
+        * babbling_kinematics: kinematics resulting from given activations
+        * babbling_activations: activations of babbling generated from generate_activation_values()
+    """
+
+    #number of samples to run
+    run_samples = int(np.round(babbling_seconds/timestep))
+
+    #generating random activations in range (min_in, max_in) -> These activations could fight against each other but that is okay
+    motor1_act = generate_activations(babbling_seconds, pass_chance, max_in, min_in, run_samples)
+    motor2_act = generate_activations(babbling_seconds, pass_chance, max_in, min_in, run_samples)
+
+    #concatenate motor activations in one vector with shape (run_samples, num_motors)
+    babbling_activations = np.transpose(np.concatenate([[motor1_act],[motor2_act]], axis=0))
+
+    #run babbling activations and map activations to resulting kinematics
+    [babbling_kinematics, babbling_activations, _] = run_activations(babbling_activations, timestep)
+
+    #return kinematics to activations mapping after skipping setup phase
+    return babbling_kinematics[skip_rows:,:], babbling_activations[skip_rows:, :]
+
+def generate_activations(signal_duration: int, pass_chance: float, max_in: float, min_in: float, run_samples: int):
+
+    """
+    Generating random activations for each motor
+    
+    Parameters
+    ----------
+        * signal_duration: duration of signal in seconds
+        * pass_chance: probability of activation being passed to motor
+        * max_in: maximum activation value
+        * min_in: minimum activation value
+        * run_samples: number of samples to run
+    
+    Returns
+    -------
+        * generated_activations: activations that can be run on robot
+    """
+
+    #generate evenly spaced samples
+    samples = np.linspace(0, signal_duration, run_samples)
+
+    #initialize activations vector
+    generated_activations = np.zeros(run_samples,)
+
+	#generating random activations
+    for i in range(1, run_samples):
+        pass_rand = np.random.uniform(0,1,1)
+	
+        if pass_rand < pass_chance:
+            #generate random activation in range (min_in, max_in)
+            generated_activations[i] = ((max_in - min_in) * np.random.uniform(0,1,1)) + min_in
+        else:
+            #set activation to previous activation
+            generated_activations[i] = generated_activations[i-1]
+
+    return generated_activations
\ No newline at end of file
diff --git a/controllers/g2p-closed-loop/src/run.py b/controllers/g2p-closed-loop/src/run.py
new file mode 100644
index 0000000..b553d4e
--- /dev/null
+++ b/controllers/g2p-closed-loop/src/run.py
@@ -0,0 +1,55 @@
+import numpy as np
+import sklearn
+import rospy
+
+def run_activations(activations: np.ndarray, timestep: int):
+
+	"""
+	Running activations on test bench
+
+	Parameters
+	----------
+		* activations: activations to run on test bench
+		* timestep: duration of each timestep in seconds
+
+	Returns
+	-------
+		* actual_kinematics: kinematics resulting from given activations
+		* actual_activations: activations that were run on test bench
+		* actual_tendon_forces: tendon forces that were measured on test bench
+	"""
+
+	#number of activations
+	num_activations = activations.shape[0]
+
+	#actual angles of test bench joint
+	actual_angles = np.zeros((num_activations,1))
+
+	#actual tendon forces of test bench tendons (felx, extend)
+	actual_tendon_forces = np.zeros((num_activations,2))
+
+	#actual activations of test bench motors (felx, extend)
+	actual_activations = np.zeros((num_activations,2))
+
+	#iterate over activations and run test bench with it
+	for i in range(num_activations):
+
+		#TODO: Send activation to test bench
+		#Here we would run the test bench with the activation
+		#Check Nils code for how to do that
+		#Current problem: Motors are not backdrivable -> How can we avoid tendon slackness? Valero Lab had backdrivable motors
+
+		msg = BenchMotorControl()
+		msg.flex_myobrick_pwm = activations[i,0]
+		msg.extend_myobrick_pwm = activations[i,1]
+		publisher.publish(msg)
+
+		#appending positions and activations to arrays
+		actual_angles[i,:] = 0 # Todo get angle from test bench
+
+		pass
+
+	#calculate kinematics from angles
+	actual_kinematics = calculate_kinematics(angles = actual_angles, timestep = timestep)
+
+	return actual_kinematics, actual_activations, actual_tendon_forces
\ No newline at end of file