2015/02/10 by Michele Matteini, Matteini, Michele
Computer Science · Engineering · #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1502.02844
openalex publication_date 2015/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we will explore different available methodologies to automatically design controllers for tasks that spans many level of abstraction, where the gap between primitive behaviours and the task definition is high. A good understanding of your evolutionary setup is needed to choose the correct strategy with which to tackle complex tasks thus we'll first review the most used types of each element composing an evolutionary setup (controllers, objective functions, ect.) then we'll move the focus on the bootstrapping problem and on the different strategies used to overcome it.