Student Thesis STUD-2443

BibliographyZielke, Viktor: Instance-Based Learning of Affordances.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Student Thesis No. 2443 (2014).
47 pages, english.
CR-SchemaI.2.10 (Vision and Scene Understanding)
I.4.8 (Image Processing and Computer Vision Scene Analysis)
I.5.2 (Pattern Recognition Design Methodology)
Abstract

The discovery of possible interactions with objects is a vital part of an exploration task for robots. An important subset of these possible interactions are affordances. Affordances describe what a specific object can afford to a specific agent, based on the capabilities of the agent and the properties of the object in relation to the agent. For example, a chair affords a human to be sat-upon, if the sitting area of the chair is approximately knee-high. In this work, an instance-based learning approach is made to discover these affordances solely through different visual representations of point cloud data of an object. The point clouds are acquired with a Microsoft Kinect sensor. Different representations are tested and evaluated against a set of point cloud data of various objects found in a living room environment.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Parallel Systems
Superviser(s)Otte, Stefan
Entry dateJune 23, 2014
   Publ. Computer Science