A review on robot learning and controlling: imitation learning and human-computer interaction

Wang, Qizhi; Xu, De; Shi, LuYan

doi:10.1109/ccdc.2013.6561428

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…The objectives of the blended learning environment include rich teaching content, access to knowledge, social interaction, personal initiative, and cost-effectiveness. The discussion of e-learning and web-based learning focuses on hybrid learning programs, which can include offline and online learning, selfpaced and on-site cooperative learning, structured and unstructured learning, customized content and ready-made content [23]. Blended learning combines basic information about novice technology with complex ideas about using technology in the classroom.…”

Section: E Research On Ai Hybrid Learning In Hementioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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Artificial intelligence (AI), as a significant feature of modern development, has changed the form of education, especially with a significant impact on higher education (HE). Clarifying the integration and infiltration of HE and AI is of great significance for the future development of AI in HE. The Citespace is used to make a quantitative and qualitative analysis of the indicators such as authors, institutions, nationalities, disciplines, journals, and highly cited articles of AI research literature -located at HE included in the Web of Science core collection database. The results show that in recent years, AI in the field of HE has received extensive international attention, and the research results are in a growing trend. In the field of international HE, AI focuses on hot topics such as parameter design, deep learning, machine learning human-computer interaction and hybrid learning. And deep learning, learning space simulation design, learning effectiveness measurement, etc. will become an important development direction of international AI research in the field of HE.

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Section: E Research On Ai Hybrid Learning In Hementioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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“…As industrial automation will have the potential to reduce human intervention in dangerous or harmful places such as chemical production industries, nuclear energy section and many more. Over the last decade, robotic systems 1 Corresponding Author. y.jadeja@derby.ac.uk 2 Corresponding Author.…”

Section: Introductionmentioning

confidence: 99%

“…mshafik@derby.ac.uk have become sophisticated, powerful and, are increasingly moving toward more serviceoriented positions. Imitation learning and human-robotic systems interaction are crucial for enhancing robotics cells intelligence and ability to collaborate with humans [1]. Imitation Learning is a technique for learning a behaviour policy by imitating it.…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Design of Self-Learning Robotic System Using Imitation Learning

Jadeja

Shafik

Wood

2022

Advances in Transdisciplinary Engineering

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Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Of course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. Further work is still undertaken, and this will focus on learning from more than one expert, optimisation, impact of dynamic manufacturing environment.

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“…In the past few decades, people have been looking for an effective way to interact with the computer without using a traditional input device such as keyboard or mouse. There are many HCI modalities such as hand gesture, pointing gesture, gazing orientation, speech and so forth [2–4]. Among these modalities, hand gesture [5–7] is an intuitive modality to perform HCI, but there exists some limitations for HCI due to the complexity of hand gesture.…”

Section: Introductionmentioning

confidence: 99%