- 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." -
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