A Multimodal Hierarchial Approach to Robot Learning by Imitation

In this paper we propose an approach to robot learning by imitation that uses the multimodal inputs of language, vision and motor. In our approach a student robot learns from a teacher robot how to perform three separate behaviours based on these inputs. We considered two neural architectures for performing this robot learning. First, a one-step hierarchial architecture trained with two different learning approaches either based on Kohonen's self-organising map or based on the Helmholtz machine turns out to be inefficient or not capable of performing differentiated behavior. In response we produced a hierarchial architecture that combines both learning approaches to overcome these problems. In doing so the proposed robot system models specific aspects of learning using concepts of the mirror neuron system (Rizzolatti and Arbib, 1998) with regards to demonstration learning

Neural Control of Actions Involving Different Coordinate Systems

di Parm

Is one business and management school better than another? A clustering perspective across UK national HE league tables

Higher education (HE) league table rankings have been widely adopted and used by stakeholders such as students and HE institution managers. Nevertheless, criticisms have been raised by researchers. The present study proposes that rather than using league table indicators to make a single, standardised ranking list, indicators of the existing three UK national league tables indicators could be used to form clusters based on the homogeneity of the characteristics of each business and management school. Six groups of business and management schools were extracted and characterised. The approach has removed the notion of saying that one business and management school is better than another (single ranking). Findings offer stakeholders a clearer view of business and management school by identifying groups that best represent the UK business and management school focus

Reinforcement Learning Embedded in Brains and Robots

In many ways and in various tasks, computers are able to outperform humans. They can store and retrieve much larger amounts of data or even beat humans at chess. However, when looking at robots they are still far behind even a small child in terms of their performance capabilities. Even a sophisticated robot, such as ASIMO, is limited to mostl