diff --git a/doc/rcfs.pdf b/doc/rcfs.pdf
index 6e66cf92d5dee741242c8d7821e8a46e21050f1f..8dc1b0611350433e08269f3528baaa55a1ff7b63 100644
Binary files a/doc/rcfs.pdf and b/doc/rcfs.pdf differ
diff --git a/doc/rcfs.tex b/doc/rcfs.tex
index 881bfcaef381d99ac4f8f791d58e6af035a2535e..70b287459f2ee71f003a1adc54aa279d8d7c8d56 100644
--- a/doc/rcfs.tex
+++ b/doc/rcfs.tex
@@ -96,8 +96,8 @@ innerleftmargin=0.3em,innerrightmargin=0.3em,innertopmargin=0.3em,innerbottommar
 %    urlcolor=black
 %}
 
-\title{\huge Learning and Optimization in Robotics\\[4mm]\emph{Lecture notes}}
-%Math Cookbook for robot manipulation  
+\title{\huge A Math Cookbook for Robot Manipulation}
+%Learning and Optimization in Robotics\\[4mm]\emph{Lecture notes}
 %A practical guide to learning and control problems in robotics\\solved with second-order optimization
 \author{Sylvain Calinon, Idiap Research Institute}
 \date{}
@@ -114,7 +114,7 @@ innerleftmargin=0.3em,innerrightmargin=0.3em,innertopmargin=0.3em,innerbottommar
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Introduction}
 
-This technical report presents several learning and optimal control techniques in robotics in the form of simple toy problems that can be easily coded. It comes as part of \textbf{Robotics Codes From Scratch (RCFS)}, a website containing interactive sandbox examples and exercises, together with a set of standalone source code examples gathered in a git repository, which can be accessed at:
+This cookbook presents several learning and optimal control recipes for robotics (essentially for robot manipulators), complemented by simple toy problems that can be easily coded. It accompanies \textbf{Robotics Codes From Scratch (RCFS)}, a website containing interactive sandbox examples and exercises, together with a set of standalone source code examples gathered in a git repository, which can be accessed at:
 \begin{center}
 \url{https://rcfs.ch}
 %\url{https://gitlab.idiap.ch/rli/robotics-codes-from-scratch}
@@ -1004,7 +1004,7 @@ which ensures that $w_4=w_5$ and $w_6=-w_3+2w_5$. These constraints guarantee th
 
 Based on observed data $\bm{x}$, the superposition weights $\bm{\hat{w}}$ can be estimated as a simple least squares estimate 
 \begin{equation}
-	\bm{\hat{w}} = \bm{\Psi}^\psin \bm{x} = {(\bm{\Psi}^\trsp\bm{\Psi}}^{-1} \bm{\Psi}^\trsp \bm{x},
+	\bm{\hat{w}} = \bm{\Psi}^\psin \bm{x} = {(\bm{\Psi}^\trsp\bm{\Psi})}^{-1} \bm{\Psi}^\trsp \bm{x},
 \end{equation}
 or as the regularized version (ridge regression)
 \begin{equation}