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To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate modes and different modes correspond to different hierarchical structure of the controller. This...
Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle real-world sequential decision processes but require a known model to be solved by most approaches. However, mainstream POMDP research focuses on the discrete case and this complicates its application to most realistic problems that are naturally modeled using continuous state spaces. In this paper,...
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