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Active learning of manipulation sequences
2014
2014 IEEE International Conference on Robotics and Automation (ICRA)
We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre-and postconditions. Learned by model-based reinforcement
doi:10.1109/icra.2014.6907693
dblp:conf/icra/MartinezAJTRWAHP14
fatcat:lynppimeejguvar5pmgswwry4q