Publication
Active learning of manipulation sequences
Conference Article
Conference
IEEE International Conference on Robotics and Automation (ICRA)
Edition
2014
Pages
5671-5678
Doc link
http://dx.doi.org/10.1109/ICRA.2014.6907693
File
Authors
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Martínez Martínez, David
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Alenyà Ribas, Guillem
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Jiménez Schlegl, Pablo
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Torras Genís, Carme
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Rossmann, Jürgen
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Wantia, Nils
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Eren Erdal, Aksoy
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Haller, Simon
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Piater, Justus
Projects associated
Abstract
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.
Categories
learning (artificial intelligence), planning (artificial intelligence), uncertainty handling.
Scientific reference
D. Martínez, G. Alenyà, P. Jiménez, C. Torras, J. Rossmann, N. Wantia, A. Eren Erdal, S. Haller and J. Piater. Active learning of manipulation sequences, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, China, pp. 5671-5678.
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