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Towards Robot-Centric Conceptual Knowledge Acquisition
Robots require knowledge about objects in order to efficiently perform v...
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MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations
Extracting and binding salient information from different sensory modali...
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Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully...
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Teaching Robots Novel Objects by Pointing at Them
Robots that must operate in novel environments and collaborate with huma...
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Deliberative and Conceptual Inference in Service Robots
Service robots need to reason to support people in daily life situations...
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Metaknowledge Extraction Based on Multi-Modal Documents
The triple-based knowledge in large-scale knowledge bases is most likely...
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Learning Kinematic Descriptions using SPARE: Simulated and Physical ARticulated Extendable dataset
Next generation robots will need to understand intricate and articulated...
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From Multi-modal Property Dataset to Robot-centric Conceptual Knowledge About Household Objects
Tool-use applications in robotics require conceptual knowledge about objects for informed decision making and object interactions. State-of-the-art methods employ hand-crafted symbolic knowledge which is defined from a human perspective and grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, their conceptual understanding of objects must be generated from a robot's perspective entirely, which asks for robot-centric conceptual knowledge about objects. With this goal in mind, this article motivates that such knowledge should be based on physical and functional properties of objects. Consequently, a selection of ten properties is defined and corresponding extraction methods are proposed. This multi-modal property extraction forms the basis on which our second contribution, a robot-centric knowledge generation is build on. It employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising physical and functional property data of 110 houshold objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property extraction methods, the semantics of the considered properties within the dataset and its usefulness in real-world applications such as tool substitution.
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