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We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. In order to avoid periodic batch steps, we use the software iSAM2 for sparse nonlinear incremental optimization, which is highly efficient through incremental variable reordering and fluid relinearization. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. The approach relies on the sparse representation of each pixel by a weighted linear combination of the training data. Ariane Lange.
TC Teisnach Sensorik
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