From Multi-modal Property Dataset to Robot-centric Conceptual Knowledge About Household Objects
This work addresses the need for robots to have their own conceptual understanding of objects, rather than relying on human-centric knowledge, which is incremental as it builds on existing property extraction and clustering methods.
The paper tackles the problem of generating robot-centric conceptual knowledge for tool-use by proposing a framework that extracts physical and functional properties from household objects and uses unsupervised clustering to create symbols, resulting in a dataset of 110 objects evaluated for tool substitution.
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.