Jakub Rozlivek;Alessandro Roncone;Ugo Pattacini;Matej Hoffmann
{"title":"HARMONIOUS—Human-Like Reactive Motion Control and Multimodal Perception for Humanoid Robots","authors":"Jakub Rozlivek;Alessandro Roncone;Ugo Pattacini;Matej Hoffmann","doi":"10.1109/TRO.2024.3502216","DOIUrl":null,"url":null,"abstract":"For safe and effective operation of humanoid robots in human-populated environments, the problem of commanding a large number of degrees of freedom (DoFs) while simultaneously considering dynamic obstacles and human proximity has still not been solved. In this article, we present a new reactive motion controller that commands two arms of a humanoid robot and three torso joints (17 DoF in total). We formulate a quadratic program that seeks joint velocity commands respecting multiple constraints while minimizing the magnitude of the velocities. We introduce a new unified treatment of obstacles that dynamically maps visual and proximity (precollision) and tactile (postcollision) obstacles as additional constraints to the motion controller, in a distributed fashion over the surface of the upper body of the iCub robot (with 2000 pressure-sensitive receptors). This results in a bioinspired controller that: first, gives rise to a robot with whole-body visuo-tactile awareness, resembling peripersonal space representations, and, second, produces human-like minimum jerk movement profiles. The controller was extensively experimentally validated, including a physical human–robot interaction scenario.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"378-393"},"PeriodicalIF":10.5000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Robotics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10758235/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
For safe and effective operation of humanoid robots in human-populated environments, the problem of commanding a large number of degrees of freedom (DoFs) while simultaneously considering dynamic obstacles and human proximity has still not been solved. In this article, we present a new reactive motion controller that commands two arms of a humanoid robot and three torso joints (17 DoF in total). We formulate a quadratic program that seeks joint velocity commands respecting multiple constraints while minimizing the magnitude of the velocities. We introduce a new unified treatment of obstacles that dynamically maps visual and proximity (precollision) and tactile (postcollision) obstacles as additional constraints to the motion controller, in a distributed fashion over the surface of the upper body of the iCub robot (with 2000 pressure-sensitive receptors). This results in a bioinspired controller that: first, gives rise to a robot with whole-body visuo-tactile awareness, resembling peripersonal space representations, and, second, produces human-like minimum jerk movement profiles. The controller was extensively experimentally validated, including a physical human–robot interaction scenario.
期刊介绍:
The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles.
Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.