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

A Mirror Neuron Inspired Hierarchical Network for Action Selection. In Biomimetic neural learning for intelligent robots

1 Introduction One learning approach that can make intelligent robots easy to use is imitation learning. Such learning allows the observer to gain skills by creating an abstract representation of the teacher's behaviour, and an understanding of the teacher's aims to produce the required solution [5]. There is growing interest in imitation learning as it offers a flexible way to programme robots by having the robot observe and imitate either another robot or a human. Multimodal inputs are used in our robot learning model as it is only through the combination of language, vision and motor actions, that robots will be able to become service robots to benefit humans. By combining multimodal inputs service robots should adapt to changes in their environment and improve their decision-making. For instance, a robot performs grasping operations based on language, gestures and vision [12]. Language can be acquired by pairing words with raw multimodal sensory data [11]. A mirror neuron approach using multimodal inputs was applied to predictive behaviour perception and imitation [2]. Our approach incorporates a language element as input to the mirror neuron system to achieve learning by imitation

An associator network approach to robot learning by imitation through vision, motor control and language

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A Multimodal Hierarchical Approach to Robot

Abstract 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 onestep hierarchical 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 behaviour. In response we produced a hierarchical 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. 1. Introduction Intelligent robots that are easy to use require a learning approach such as imitation learning which allows the observer to gain skills, by creating an abstract representation of the teacher's behaviour, understand the aims of the teacher and produce the solution (Infantino et al., 2003). There is growing interest in imitation as it offers a flexible way to programme robots by having the robot observe and imitate either another robot or a human. Multimodal inputs are used in our robot learning model as it is only through the combination of language, vision and motor actions, that robots will be able to become service robots to benefit humans. By combining multimodal inputs social robots should adapt to changes in their environment and improve their decision-making

Mechanisms of acute right ventricular injury in cardiothoracic surgical and critical care settings, Part 1

Right ventricular biomechanics play a key role in maintaining cardiovascular homeostasis in cardiothoracic surgical settings. The interplay between the right ventricle and pulmonary vasculature, the so called ventriculo-arterial coupling determines the response of the right ventricle to different loading conditions and its interaction with the left ventricle in order to meet flow demand. There is a lack of universal right ventricular injury definition since it represents a range of abnormal right ventricular biomechanics and phenotypes: from diastolic dysfunction to right ventricular failure and shock. Understanding the mechanisms of uncoupling between the right ventricle and pulmonary circulation as well as primary right ventricular insult may inform future research, particularly phenotyping of right ventricular injury which may aid in individualizing right ventricle-targeted therapies. In this narrative review, the authors discuss the mechanisms of right ventricular injury in cardiac and thoracic surgical settings and implications for practice

Mechanisms of acute right ventricular injury in cardiothoracic surgical and critical care settings: part 2

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