diff --git a/spider_control/control.py b/spider_control/control.py
index 93701561afd6373af72ccc6067261cb2420ae374..b758dcebafa00f4dd66b86ce4f0cc641d7940823 100644
--- a/spider_control/control.py
+++ b/spider_control/control.py
@@ -1,10 +1,10 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3.6.7
 import tensorflow as tf
 import numpy as np
 import roslib
 roslib.load_manifest('joint_states_listener')
 roslib.load_manifest('spider_control')
-import pylab as plt
+#import pylab as plt
 import math
 import threading
 from geometry_msgs.msgs import *
@@ -16,8 +16,8 @@ import threading
 import time
 from std_msgs.msg import Float64
 from std_msgs.msg import Header
-from sklearn.multiclass import OneVsRestClassifier
-from sklearn.svm import LinearSVC
+#from sklearn.multiclass import OneVsRestClassifier
+#from sklearn.svm import LinearSVC
 
 tf.enable_eager_execution()
 
@@ -120,12 +120,12 @@ def call_return_joint_states(joint_names):
     try:
         s = rospy.ServiceProxy("return_joint_States", ReturnJointStates)
         resp = s(joint_names)
-    except rospy.ServiceException, e:
-        print"error when calling return_joint_states: %s"%e
+    except rospy.ServiceException as e:
+        print("error when calling return_joint_states: %s"%e)
         sys.exit(1)
     for (ind, joint_name) in enumerate(joint_names):
         if(not resp.found[ind]):
-            print"joint %s not found"%joint_name
+            print("joint %s not found"%joint_name)
     return resp.position, resp.velocity, resp.effort
 
 def talker(jointname , position):
@@ -192,8 +192,17 @@ class  Leg_attribute:
     def y_right_com(self, a, b, c, d, one_one, one_two, one_three, two_one, two_two, two_three, three_one, three_two, three_three, four_one, four_two, four_three, five_one, five_two, five_three, six_one, six_two, six_three, seven_one, seven_two, seven_three, eight_one, eight_two, eight_three):
         return (1/(2*(a+6*b)))*(3*a*d+2*b*c*(3*math.sin(two_three)+3*math.sin(four_three)+3*math.sin(six_three)+3*math.sin(eight_three)+2*math.sin(two_two)+2*math.sin(four_two)+2*math.sin(six_two)+2*math.sin(eight_two)+math.sin(two_one)+math.sin(four_one)+math.sin(six_one)+math.sin(eight_one)))
 
-    def x_system_com(self, a, b, c, d, one_one, one_two, one_three, two_one, two_two, two_three, three_one, three_two, three_three, four_one, four_two, four_three, five_one, five_two, five_three, six_one, six_two, six_three, seven_one, seven_two, seven_three, eight_one, eight_two, eight_three):
-        return  (1/(a+6*b*c))*(2*a*d+b*c*(3*(math.cos(one_three)+math.cos(two_three)+math.cos(three_three)+math.cos(four_three)+math.cos(five_three)+math.cos(six_three)+math.cos(seven_three)+math.cos(eight_three))+2*(math.cos(one_two)+math.cos(two_two)+math.cos(three_two)+math.cos(four_two)+math.cos(five_two)+math.cos(six_two)+math.cos(seven_two)+math.cos(eight_two))+math.cos(one_one)+math.cos(two_one)+math.cos(three_one)+math.cos(four_one)+math.cos(five_one)+math.cos(six_one)+math.cos(seven_one)+math.cos(eight_one)))
+    def x_system_com(self, a, b, c, d, one_one, one_two, one_three, two_one, two_two, two_three, three_one, three_two,
+                     three_three, four_one, four_two, four_three, five_one, five_two, five_three, six_one, six_two,
+                     six_three, seven_one, seven_two, seven_three, eight_one, eight_two, eight_three):
+        return  (1/(a+6*b*c))*(2*a*d+b*c*(3*(math.cos(one_three) + math.cos(two_three) + math.cos(three_three) +
+                                             math.cos(four_three) + math.cos(five_three) + math.cos(six_three) +
+                                             math.cos(seven_three) + math.cos(eight_three)) + 2*(math.cos(one_two) +
+                                             math.cos(two_two) + math.cos(three_two) + math.cos(four_two) +
+                                             math.cos(five_two) + math.cos(six_two) + math.cos(seven_two) +
+                                             math.cos(eight_two)) + math.cos(one_one) + math.cos(two_one) +
+                                             math.cos(three_one) + math.cos(four_one) + math.cos(five_one) +
+                                             math.cos(six_one) + math.cos(seven_one) + math.cos(eight_one)))
 
     def y_system_com(self, a, b, c, d, one_one, one_two, one_three, two_one, two_two, two_three, three_one, three_two, three_three, four_onr, four_two, four_three, five_one, five_two, five_three, six_one, six_two, six_three, seven_one, seven_two, seven_three, eight_one, eight_two, eight_three):
         return  (1/(a+6*b*c))*(2*a*d+b*c*(3*(math.sin(one_three)+math.sin(two_three)+math.sin(three_three)+math.sin(four_three)+math.sin(five_three)+math.sin(six_three)+math.sin(seven_three)+math.sin(eight_three))+2*(math.sin(one_two)+math.sin(two_two)+math.sin(three_two)+math.sin(four_two)+math.sin(five_two)+math.sin(six_two)+math.sin(seven_two)+math.sin(eight_two))+math.sin(one_one)+math.sin(two_one)+math.sin(three_one)+math.sin(four_one)+math.sin(five_one)+math.sin(six_one)+math.sin(seven_one)+math.sin(eight_one)))
@@ -272,11 +281,11 @@ class  Architecture:
     def weight_initialize (self, neurons):
         weights1 = tf.Variable(tf.random_uniform((neurons, 1), minval=0, maxval=1, dtype=tf.float32, seed=7273))
         weights2 = tf.Variable(tf.random_uniform((neurons, 1), minval=0, maxval=1, dtype=tf.float32, seed=3000))
-        weights3h=tf.Variable(tf.random_uniform((neurons/3, neurons/6), minval=0, maxval=1, dtype=tf.float32, seed=0119))
+        weights3h=tf.Variable(tf.random_uniform((neurons/3, neurons/6), minval=0, maxval=1, dtype=tf.float32, seed= 119))
         weights3c=tf.Variable(tf.random_uniform((neurons/3, 1), minval=0, maxval=1, dtype=tf.float32, seed=1508))
         sess=tf.Session()
         weights_i, weights_o, weights_h, weights_h_c = sess.run(weights1, weights2, weights3h, weights3c)
-        return (weights_i, weights_o, weights_h, weights_h_c)
+        return weights_i, weights_o, weights_h, weights_h_c
 
     # get neuron value method helps us to write the joint angles to the individual neurons which we can later use for learning
     def get_neuron_value(self, a, b):
@@ -347,7 +356,7 @@ class MultiClassLogistic:
         self.neuron_out = tf.placeholder(tf.float32, shape=(4, 1))#four neurons in output layer because of the four quadrants
         weights1 = tf.Variable(tf.random_normal((neurons, 1), dtype=tf.float32, seed=1606))#weights of input layer
         weights2 = tf.Variable(tf.random_normal((neurons/2, neurons/2), dtype=tf.float32, seed=1003))#weights for hidden layer, number of neurons in hidden layer is identified by the quadrant concept
-        weights3 = tf.Variable(tf.random_normal((4, 2), dtype=tf.float32, seed=0207))#weights for output layer
+        weights3 = tf.Variable(tf.random_normal((4, 2), dtype=tf.float32, seed= 207))#weights for output layer
         bias_in  =tf.random_normal((neurons, 1), dtype = tf.float32, seed = 2009)
         bias_hid = tf.random_normal((neurons/2, 1), dtype = tf.float32, seed = 2509)
         bias_out = tf.random_normal((4, 1), dtype=tf.float32, seed = 1234)
@@ -370,7 +379,7 @@ class MultiClassLogistic:
             out = sess.run(output)
         return out
 
-    def output_func(self, ):
+    #def output_func(self, ):
 
     def activation(self, a):
         'where a is the input of the neuron and the activation function is modelled as rectified linear output'
@@ -449,7 +458,7 @@ if __name__=="__main__":
     model_2 = Architecture(3, 24) #for model two of the terrain
     model_3 = Architecture(3, 24) #for model three of the terrain
     model_4 = Architecture(3, 24) #for model four of the terrain
-    run_method = raw_input("How do you want to run the algorithm ? for training, type train. for testing, type test")
+    run_method = input("How do you want to run the algorithm ? for training, type train. for testing, type test")
     knowledge_transfer = MultiClassLogistic(8, 3)
     g = 9.8
     reward_history = np.array([0])
@@ -459,7 +468,7 @@ if __name__=="__main__":
     l = Leg_attribute.l_1
     global run_time
     global model_trained
-    run_time_time = raw_input("please enter how long you intend to run the robot")
+    run_time_time = input("please enter how long you intend to run the robot")
     'giving initial angles '
     initial_angles = np.random.randint(0, 3014, size=24)
     number = 0
@@ -488,9 +497,9 @@ if __name__=="__main__":
         talker(spiderJoint8Name, initial_angles[number])
         number+=1
     if ( run_method == 'train'):
-        training_time = raw_input("please enter how long you intend to train the algorithm")
+        training_time = input("please enter how long you intend to train the algorithm")
         run_time = training_time
-        model_trained = raw_input("which model do you want to train ?, please enter model_number for straight terrain select 1, for accute angled terrain select 2, for obtuse angled terrain select 3, for up/down terrain select 4")
+        model_trained = input("which model do you want to train ?, please enter model_number for straight terrain select 1, for accute angled terrain select 2, for obtuse angled terrain select 3, for up/down terrain select 4")
         output = one_hot_encoding(run_time, model_trained)
         pref_out = tf.placeholder(tf.float32, shape=(4, 1))
         with tf.Session as sess1:
diff --git a/spider_description/urdf/spider.xacro b/spider_description/urdf/spider.xacro
index 92e8a7533da4fc2711c4719e2db979b9f320cba5..0fb3ab486bf49c4e7f192a63c235a1a4680a4909 100644
--- a/spider_description/urdf/spider.xacro
+++ b/spider_description/urdf/spider.xacro
@@ -13,7 +13,7 @@
   <xacro:property name="height_link_1_2" value="4"/> <!--to adjust the error as I misspelled height_link1_2-->
   <xacro:property name="height_link_3" value="6" /> <!-- Link 3 -->
   <xacro:property name="axel_offset" value="0" /> <!-- Space btw top of beam and the each joint -->
-
+  <xacro:property name="tactile_link" value="1"/><!--size of the tactile sensor-->
   <!-- Import all Gazebo-customization elements, including Gazebo colors -->
   <xacro:include filename="$(find spider_description)/urdf/spider.gazebo" />
   <!-- Import Rviz colors -->
@@ -953,7 +953,7 @@
   <joint name="tactile_2" type="fixed">
     <parent link="left_torso_2_3"/>
     <child link="tactile_sensor_2"/>
-    <origin xyz="0 0 ${height_link_2_2}" rpy="0 0 0"/>
+    <origin xyz="0 0 ${height_link_1_2}" rpy="0 0 0"/>
     <axis xyz="0 1 0"/>
     <dynamics damping="0.7" friction="0.0"/>
   </joint>
@@ -984,7 +984,7 @@
   <joint name="tactile_3" type="fixed">
     <parent link="left_torso_3_3"/>
     <child link="tactile_sensor_3"/>
-    <origin xyz="0 0 ${height_link_3_2}" rpy="0 0 0"/>
+    <origin xyz="0 0 ${height_link_1_2}" rpy="0 0 0"/>
     <axis xyz="0 1 0"/>
     <dynamics damping="0.7" friction="0.0"/>
   </joint>
@@ -1015,7 +1015,7 @@
   <joint name="tactile_4" type="fixed">
     <parent link="left_torso_4_3"/>
     <child link="tactile_sensor_4"/>
-    <origin xyz="0 0 ${height_link_4_2}" rpy="0 0 0"/>
+    <origin xyz="0 0 ${height_link_1_2}" rpy="0 0 0"/>
     <axis xyz="0 1 0"/>
     <dynamics damping="0.7" friction="0.0"/>
   </joint>
@@ -1046,7 +1046,7 @@
   <joint name="tactile_5" type="fixed">
     <parent link="right_torso_1_3"/>
     <child link="tactile_sensor_5"/>
-    <origin xyz="0 0 ${height_link_5_2}" rpy="0 0 0"/>
+    <origin xyz="0 0 ${height_link_1_2}" rpy="0 0 0"/>
     <axis xyz="0 1 0"/>
     <dynamics damping="0.7" friction="0.0"/>
   </joint>
@@ -1077,7 +1077,7 @@
   <joint name="tactile_6" type="fixed">
     <parent link="right_torso_2_3"/>
     <child link="tactile_sensor_6"/>
-    <origin xyz="0 0 ${height_link_6_2}" rpy="0 0 0"/>
+    <origin xyz="0 0 ${height_link_1_2}" rpy="0 0 0"/>
     <axis xyz="0 1 0"/>
     <dynamics damping="0.7" friction="0.0"/>
   </joint>