The grammar is learned by applying a Markov chain Monte Carlo optimization over the posteriors of the grammars given the observations. The proposal distribution is defined as a mixture over the probabilities of the operators connecting the search space.
Nov 17, 2019
In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks ...
Additionally, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks ...
We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of ...
Oct 22, 2024 · In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive ...
Each subtask is represented as movement primitive and the main task is solved by a sequence of primitives. By defining a general set of attributes and a ...
In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks ...
We show how such grammars can be applied to solve complex tasks by sequencing simpler subtasks. Each subtask is represented as movement primitive and the main ...
Author(s):, Lioutikov, R. and Maeda, G. and Veiga, F.F. and Kersting, K. and Peters, J. ; Journal: The International Journal of Robotics Research (IJRR) ; Volume: ...
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Learning attribute grammars for movement primitive sequencing
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Learning attribute grammars for movement primitive sequencing. Autor(en):, Lioutikov, Rudolf; Maeda, Guilherme; Veiga, Filipe; Kersting, Kristian; Peters, Jan.