diff --git a/_layouts/default.html b/_layouts/default.html
index 37bfb7c..c7b86ce 100644
--- a/_layouts/default.html
+++ b/_layouts/default.html
@@ -27,7 +27,10 @@
       <h2 class="project-tagline">{{ page.description | default: site.description | default: site.github.project_tagline }}</h2>
       {% if site.github.is_project_page %}
         <a href="{{ site.github.repository_url }}" class="btn">View on GitHub</a>
+        <a href="https://arxiv.org/" class="btn">View on Arxiv</a>
         <a href="https://github.com/robotlearn/pyrobolearn/tree/master/examples" class="btn">View Examples</a>
+        <a href="https://github.com/robotlearn/pyrobolearn/tree/master/docs" class="btn">View Documentation</a>
+        <a href="https://www.youtube.com/playlist?list=PL7luWwjSAezVxBTindZWUo7hn-Dj9w3gL" class="btn">View on Youtube</a>
       {% endif %}
       {% if site.show_downloads %}
         <a href="{{ site.github.zip_url }}" class="btn">Download .zip</a>
diff --git a/index.md b/index.md
index a171f58..151a8f7 100644
--- a/index.md
+++ b/index.md
@@ -6,15 +6,9 @@ into a common place are scarce. Existing ones tend to be robot-specific, and oft
 other robots. 
 Also, their architecture is often weakly structured, mainly because of a lack of modularity and flexibility. This leads users 
 to reimplement several pieces of code to integrate them into their own experimental or benchmarking work. 
-To overcome these issues, we introduce PyRoboLearn, a new Python robot learning framework that combines different learning 
+To overcome these issues, we introduce *PyRoboLearn*, a new Python robot learning framework that combines different learning 
 paradigms into a single framework. Our framework provides a plethora of robotic environments, learning models and algorithms. 
-PyRoboLearn is developed with a particular focus on modularity, flexibility, generality, and simplicity to favor (re)usability. 
+*PyRoboLearn* is developed with a particular focus on modularity, flexibility, generality, and simplicity to favor (re)usability. 
 This is achieved by abstracting each key concept, undertaking a modular programming approach, minimizing the coupling among the 
 different modules, and favoring composition over inheritance for better flexibility. We demonstrate the different features and 
 utility of our framework through different use cases.
-
-- Paper
-- [Github repository](https://github.com/robotlearn/pyrobolearn)
-- [Documentation](https://github.com/robotlearn/pyrobolearn/tree/master/docs)
-- [Examples](https://github.com/robotlearn/pyrobolearn/tree/master/examples)
-- [Videos](https://www.youtube.com/playlist?list=PL7luWwjSAezVxBTindZWUo7hn-Dj9w3gL)