Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 32, Issue 14, Pages 1742-1766Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364913509859
Keywords
Life-long learning; topological mapping; topic models; perplexity
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Funding
- Rhodes Trust, Oxford, UK
- EPSRC [EP/I005021/1]
- Engineering and Physical Sciences Research Council [EP/I005021/1] Funding Source: researchfish
- EPSRC [EP/I005021/1] Funding Source: UKRI
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In this work, we present a novel approach that allows a robot to improve its own navigation performance through introspection and then targeted data retrieval. It is a step in the direction of life-long learning and adaptation and is motivated by the desire to build robots that have plastic competencies which are not baked in. They should react to and benefit from use. We consider a particular instantiation of this problem in the context of place recognition. Based on a topic-based probabilistic representation for images, we use a measure of perplexity to evaluate how well a working set of background images explain the robot's online view of the world. Offline, the robot then searches an external resource to seek out additional background images that bolster its ability to localize in its environment when used next. In this way the robot adapts and improves performance through use. We demonstrate this approach using data collected from a mobile robot operating in outdoor workspaces.
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