{"title":"A general approach for generating artificial human-like motions from functional components of human upper limb movements","authors":"Marco Baracca , Giuseppe Averta , Matteo Bianchi","doi":"10.1016/j.conengprac.2024.105968","DOIUrl":null,"url":null,"abstract":"<div><p>Anthropomorphism of artificial systems is a key enabling factor to ensure effective and compelling human–machine interactions in different domains, including immersive extended reality environments and cobotics applications. Among the different aspects that anthropomorphism refers to, the generation of human-like motions plays a crucial role. To this aim, optimization-based techniques, whose functional cost is devised from neuroscientific findings, or learning-based approaches have been proposed in literature. However, these methods come with limitations, e.g., limited motion variability or the need for high dimensional datasets. In previous works of our group, we proposed to exploit functional Principal Component Analysis (fPCA) of human upper limb movements, to extract principal motion modes in the joint domain and use them to directly embed the human-like behaviour in the planning algorithm. However, this approach faces with translational issues related to the computational burden and to the application to kinematic structures different from the one used to describe human movements. To overcome this problem, we propose a general framework to generate human-like motion directly in the Cartesian domain by exploiting fPCA. This solution permits to perform obstacle avoidance with low computational time and it can be applied to any kinematic chain. To prove the effectiveness of our approach, we tested it against a state-of-the-art human-like planning algorithm both in terms of the accuracy of target reaching and human-likeness features of the generated movement.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S096706612400128X/pdfft?md5=2db1747ddc22a352c9a9dc2a98577733&pid=1-s2.0-S096706612400128X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706612400128X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Anthropomorphism of artificial systems is a key enabling factor to ensure effective and compelling human–machine interactions in different domains, including immersive extended reality environments and cobotics applications. Among the different aspects that anthropomorphism refers to, the generation of human-like motions plays a crucial role. To this aim, optimization-based techniques, whose functional cost is devised from neuroscientific findings, or learning-based approaches have been proposed in literature. However, these methods come with limitations, e.g., limited motion variability or the need for high dimensional datasets. In previous works of our group, we proposed to exploit functional Principal Component Analysis (fPCA) of human upper limb movements, to extract principal motion modes in the joint domain and use them to directly embed the human-like behaviour in the planning algorithm. However, this approach faces with translational issues related to the computational burden and to the application to kinematic structures different from the one used to describe human movements. To overcome this problem, we propose a general framework to generate human-like motion directly in the Cartesian domain by exploiting fPCA. This solution permits to perform obstacle avoidance with low computational time and it can be applied to any kinematic chain. To prove the effectiveness of our approach, we tested it against a state-of-the-art human-like planning algorithm both in terms of the accuracy of target reaching and human-likeness features of the generated movement.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.