A review on manipulation skill acquisition through teleoperation‐based learning from demonstration. Issue 1 (22nd February 2021)

• Record Type: Journal Article
• Authors: Si, Weiyong; Wang, Ning; Yang, Chenguang
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 1(2021)
• Page Start: 1
• Page End: 16
• Publication Date: 2021-02-22
• Subjects: manipulators -- telerobotics -- learning (artificial intelligence) -- virtual reality -- control engineering computing -- remote handling -- mobile robots; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12005
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics.