{"title":"通过轨迹相似性分析为个性化远程操作控制方案聚类用户偏好","authors":"Jennifer Molnar, Varun Agrawal, Sonia Chernova","doi":"10.3389/frobt.2024.1330812","DOIUrl":null,"url":null,"abstract":"Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants’ control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"21 24","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis\",\"authors\":\"Jennifer Molnar, Varun Agrawal, Sonia Chernova\",\"doi\":\"10.3389/frobt.2024.1330812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants’ control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.\",\"PeriodicalId\":504612,\"journal\":{\"name\":\"Frontiers in Robotics and AI\",\"volume\":\"21 24\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Robotics and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frobt.2024.1330812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Robotics and AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frobt.2024.1330812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
远距离操作机器人能否成功运行,取决于将人类运动转化为机器人运动的精心设计的控制方案;然而,单一的控制方案可能并不适合所有用户。另一方面,对于设计人员来说,要实现控制方案的个性化可能并不可行。在本文中,我们提出了一种方法,通过这种方法可以将用户划分为具有相互兼容的控制方案偏好的群体。我们要求用户在虚拟现实环境中演示控制模拟机器人的徒手动作。采集手部姿态数据,并使用 SLAM 轨迹相似性分析技术与其他用户进行比较。由此得出的成对轨迹误差度量用于根据参与者的控制动作对其进行分组,而不预先了解可能存在的控制方案偏好的数量或类型。两个不同机器人的聚类结果表明,每种情况下都能稳定地形成少量聚类,每个聚类都有自己的控制方案范例。来自参与者的调查数据证实,通过这种方法识别出的群组与参与者的控制方案理由相吻合,同时还识别出了参与者控制方案描述中的细微差别,而这些细微差别对于仅靠参与者解释其偏好的设计者来说可能并不明显。
Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis
Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants’ control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.