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Paper ThA06.3

Schuster, Martin Johannes (Technische Universität München), Jain, Dominik (TU Muenchen), Tenorth, Moritz (TU München), Beetz, Michael (Technische Universität München)

Learning Organizational Principles in Human Environments

Scheduled for presentation during the Regular Session "Intelligent Manipulation Grasping" (ThA06), Thursday, May 17, 2012, 09:00−09:15, Meeting Room 6 (Oya'te)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Domestic Robots, Personal Robots, Learning and Adaptive Systems

Abstract

In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.

 

 

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