Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Fang Wan, Chaoyang Song
{"title":"通过指内视觉重建柔软机器人触感","authors":"Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Fang Wan, Chaoyang Song","doi":"10.1002/aisy.202400022","DOIUrl":null,"url":null,"abstract":"<p>Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive <i>R</i><sup>2</sup> scores of 0.9681, 0.9415, and 0.9541 along the <i>x</i>, <i>y</i>, and <i>z</i> axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.</p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"6 10","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400022","citationCount":"0","resultStr":"{\"title\":\"Reconstructing Soft Robotic Touch via In-Finger Vision\",\"authors\":\"Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Fang Wan, Chaoyang Song\",\"doi\":\"10.1002/aisy.202400022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive <i>R</i><sup>2</sup> scores of 0.9681, 0.9415, and 0.9541 along the <i>x</i>, <i>y</i>, and <i>z</i> axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.</p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":\"6 10\",\"pages\":\"\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400022\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202400022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202400022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Reconstructing Soft Robotic Touch via In-Finger Vision
Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.