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Dogged Learning for Robots

Authors: Daniel H. Grollman; Odest Chadwicke Jenkins;

Dogged Learning for Robots

Abstract

Ubiquitous robots need the ability to adapt their behaviour to the changing situations and demands they will encounter during their lifetimes. In particular, non-technical users must be able to modify a robot's behaviour to enable it to perform new, previously unknown tasks. Learning from demonstration is a viable means to transfer a desired control policy onto a robot and mixed-initiative control provides a method for smooth transitioning between learning and acting. We present a learning system (dogged learning) that combines learning from demonstration and mixed initiative control to enable lifelong learning for unknown tasks. We have implemented dogged learning on a Sony Aibo and successfully taught it behaviours such as mimicry and ball seeking

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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
51
Top 10%
Top 1%
Top 10%
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