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Active learning of manipulation sequences
Martínez Martínez, David; Alenyà Ribas, Guillem; Jimenez Schlegl, Pablo; Torras, Carme; Rossmann, Jürgen; Wantia, Nils; Eren Erdal, Aksoy; Haller, Simon; Piater, Justus
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
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 learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
learning (artificial intelligence)
planning (artificial intelligence)
uncertainty handling.
Classificació INSPEC::Cybernetics::Artificial intelligence
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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