Fast and Robust Modelling Using a Direct Translation from a Robotic Application to Its Abstracted Behaviour

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Abstract

In traditional model-based engineering (MBE), explicit behavioural models are defined with modelling or domain-specific languages like UML or AADL. These models then refer to corresponding parts of the source code. We propose an alternative scheme, where the application's abstracting code is both the behavioural model and an integral part of the implementation. Together with a special library of explicit objects, like a periodic thread, a running application is able to export its abstracted model. That model can then be refined with our translator from application sources to state machines. As we model cyberphysical systems, the models in question can be probabilistic, non-deterministic and temporal. In order to verify our approach in practice, we have implemented the said library and adapted the translator in question. To demonstrate the potential usage, we refine a model of an application of a robot performing SLAM.

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Key words

model checking,model-based engineering,real-time

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The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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