| 1. |
- Aydemir, Alper, et al.
(author)
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Object search on a mobile robot using relational spatial information
- 2010
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In: Proc. of the 11th Int Conference on Intelligent Autonomous Systems (IAS-11). - Amsterdam : IOS Press. - 9781607506126 ; , s. 111-120
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Conference paper (peer-reviewed)abstract
- We present a method for utilising knowledge of qualitative spatial relations between objects in order to facilitate efficient visual search for those objects. A computational model for the relation is used to sample a probability distribution that guides the selection of camera views. Specifically we examine the spatial relation “on”, in the sense of physical support, and show its usefulness in search experiments on a real robot. We also experimentally compare different search strategies and verify the efficiency of so-called indirect search.
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| 2. |
- Bensch, Suna, et al.
(author)
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On ambiguity in learning from demonstration
- 2010
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In: Intelligent Autonomous Systems 11 (IAS-11). - Amsterdam : IOS Press. - 9781607506126 ; , s. 47-56
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Conference paper (peer-reviewed)abstract
- An overlooked problem in Learning From Demonstration is the ambiguity that arises, for instance, when the robot is equipped with more sensors than necessary for a certain task. Simply trying to repeat all aspects of a demonstration is seldom what the human teacher wants, and without additional information, it is hard for the robot to know which features are relevant and which should be ignored. This means that a single demonstration maps to several different behaviours the teacher might have intended. This one-to-many (or many-to-many) mapping from a demonstration (or several demonstrations) into possible intended behaviours is the ambiguity that is the topic of this paper. Ambiguity is defined as the size of the current hypothesis space. We investigate the nature of the ambiguity for different kinds of hypothesis spaces and how it is reduced by a new concept learning algorithm.
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| 3. |
- Pronobis, Andrzej, et al.
(author)
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Representing spatial knowledge in mobile cognitive systems
- 2010
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In: Intelligent Autonomous Systems 11, IAS 2010. - 9781607506126 ; , s. 133-142
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Conference paper (peer-reviewed)abstract
- A cornerstone for cognitive mobile agents is to represent the vast body of knowledge about space in which they operate. In order to be robust and efficient, such representation must address requirements imposed on the integrated system as a whole, but also resulting from properties of its components. In this paper, we carefully analyze the problem and design a structure of a spatial knowledge representation for a cognitive mobile system. Our representation is layered and represents knowledge at different levels of abstraction. It deals with complex, crossmodal, spatial knowledge that is inherently uncertain and dynamic. Furthermore, it incorporates discrete symbols that facilitate communication with the user and components of a cognitive system. We present the structure of the representation and propose concrete instantiations.
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