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 # PyRoboLearn
 
-Welcome to the main page of PyRoboLearn (PRL).
+Abstract: "On the quest for building autonomous robots, several robot learning frameworks with different functionalities have 
+recently been developed. Yet, frameworks that combine diverse learning paradigms (such as imitation and reinforcement learning) 
+into a common place are scarce. Existing ones tend to be robot-specific, and often require time-consuming work to be used with 
+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 
+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. 
+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."
+
+- Link to paper: arxiv
+- Link to Github repository: https://github.com/robotlearn/pyrobolearn
+- Link to documentation: doc
+- Link to videos: youtube
+- Link to [examples](https://github.com/robotlearn/pyrobolearn/tree/master/examples)