Updated description

README.md

 # Robotics codes from scratch (RCFS)
 
-RCFS is a collection of source codes to study robotics through simple 2D examples. Most examples are coded in Python and Matlab/Octave (full compatibility with GNU Octave). Some are also coded in C++ and Julia. The code examples have .m, .py, .cpp and .jl extensions that can be found in their respective folders matlab, python, cpp and julia. 
+Robotics codes from scratch (RCFS) is a collection of source codes to study and test learning and optimization problems in robotics through simple 2D examples. Most examples are coded in Python and Matlab/Octave (full compatibility with GNU Octave). Some are also coded in C++ and Julia. The code examples have .m, .py, .cpp and .jl extensions that can be found in their respective folders matlab, python, cpp and julia. 
 
 
 ### List of examples

python/IK.py

@@ -20,7 +20,7 @@
 '''
 
 import numpy as np
-import matplotlib.pyplot as fig
+import matplotlib.pyplot as plt
 
 # Forward kinematics for all joints (in robot coordinate system)
 def fkin0(x, param): 
@@ -49,8 +49,8 @@ param = lambda: None # Lazy way to define an empty class in python
 param.dt = 1e-2 # Time step length
 param.nbData = 50 # Number of datapoints
 param.nbVarX = 3 # State space dimension (x1,x2,x3)
-param.nbVarF = 2 # Objective function dimension (position of the end-effector
-param.l = [2,2,1] # Robot links lengths
+param.nbVarF = 3 # Objective function dimension (position and orientation of the end-effector)
+param.l = [2, 2, 1] # Robot links lengths
 
 fh = np.array([-2, 1]) #Desired target for the end-effector
 x = np.ones(param.nbVarX) * np.pi / param.nbVarX #Initial robot pose
@@ -59,13 +59,13 @@ x = np.ones(param.nbVarX) * np.pi / param.nbVarX #Initial robot pose
 ## Inverse kinematics (IK)
 # ===============================
 
-fig.scatter(fh[0], fh[1], color='r', marker='.', s=10**2) #Plot target
+plt.scatter(fh[0], fh[1], color='r', marker='.', s=10**2) #Plot target
 for t in range(param.nbData):
 	f = fkin0(x, param) #Forward kinematics (for all articulations, including end-effector)
 	J = Jkin(x, param) #Jacobian (for end-effector)
 	x += np.linalg.pinv(J) @ (fh - f[:,-1]) * 10 * param.dt #Update state 
-	fig.plot(f[0,:], f[1,:], color='k', linewidth=1) #Plot robot
+	plt.plot(f[0,:], f[1,:], color='k', linewidth=1) #Plot robot
 
-fig.axis('off')
-fig.axis('equal')
-fig.show()
+plt.axis('off')
+plt.axis('equal')
+plt.show()