
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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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
