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| + | == Integration of novel objects into Digital Twin Knowledge Bases (MA Thesis) == | ||
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| + | In this thesis, the goal is to make a robotic system learn new objects automatically. | ||
| + | The system should be able to generate the necessary models required for re-detecting it again and also consult online information sources to automatically acquire knowledge about it. | ||
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| + | The focus of the thesis would be two-fold: | ||
| + | * Develop methods to automatically infer the object class of new objects. This would include perceiving it with state of the art sensors, constructing a 3d model of it and then infer the object class from online information sources. | ||
| + | * In the second step the system should also infer factual knowledge about the object from the internet and assert it into a robotic knowledgebase. Such knowledge could for example include the category of this product, typical object properties like its weight or typical location and much more. | ||
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| + | Requirements: | ||
| + | * Knowledge about sensor data processing | ||
| + | * Interest in model construction from sensory data | ||
| + | * Work with KnowRob knowledge processing framework | ||
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| + | Contact: [[team: | ||
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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