Abstract
Current cognitive vision systems and autonomous robots are not able to flexibly adapt to novel scenes. For example, when entering a new kitchen it is relatively simple for humans to adapt to the new situation. However, there exist no methods such that a robot holds a generic kitchen model that is then adapted to the new situation. We tackle this by developing a hierarchical ontology system linking process, object, and abstract world knowledge via connecting ontologies, all defined in a formal description language.
The items and objects and their affordances in the object ontology are either learned from 3D models of the Web or from samples. We bind the features of the learned models to the concepts represented in the ontology. This enables us to actively search for objects to be expected to be seen in a kitchen scenario. The search for the objects will use the selection of cues appropriate to the relevant object. We plan to evaluate this model in three different kitchens with a mobile robot with an arm and further with Romeo, a humanoid robot designed by Aldebaran to operate in homes.
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Bruckner, D., Vincze, M., Hinterleitner, I. (2012). Towards Reorientation with a Humanoid Robot. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2011. Communications in Computer and Information Science, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34781-8_13
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DOI: https://doi.org/10.1007/978-3-642-34781-8_13
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