首页|Researchers at Chalmers University of Technology Have Reported New Data on Acute-Phase Proteins (Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation In Robotic Tasks)
Researchers at Chalmers University of Technology Have Reported New Data on Acute-Phase Proteins (Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation In Robotic Tasks)
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new study on Proteins - Acute-Phase Proteins is now available. According to news reporting originating from Gothenburg, Sweden, by NewsRx correspondents, research stated, “Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications.” Financial support for this research came from Chalmers AI Research Centre. Our news editors obtained a quote from the research from the Chalmers University of Technology, “Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this letter, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging.”
GothenburgSwedenEuropeAcute-Phase ProteinsBayesian NetworksBeta-GlobulinsBlood ProteinsCarrier ProteinsEmerging TechnologiesIron-Binding ProteinsMachine LearningNano-robotProteinsRobotRoboticsRobotsTransferrinChalmers University of Technology
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IEEE
