DEXMAN: Improving robot’s DEXterous MANipulability by learning stiffness-based human motor skills and visuo-tactile exploration

DEXMAN：通过学习基于刚度的人类运动技能和视觉触觉探索来提高机器人的灵巧操控性

• 批准号: 410916101
• 负责人: Dr. Qiang Li, Ph.D.
• 金额: --
• 依托单位: Institut für Automatisierungs- und Regelungstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/410916101?language=en
• 关键词: DEXMAN; Improving; robot; DEXterous; MANipulability

Roboticists have made huge efforts to mimic the human hand, not only from the form but also from the functionalities. However, robustly grasping and manipulating an unknown object/tool is still an open question to be thoroughly solved. In this project, we will investigate a human motor skill extraction based approach to achieve robust dexterous grasping and in-hand manipulation on a robotic arm/hand system:  A novel framework of augmented dynamic movement primitives DMPs embedding perception information for human skill extraction and generalization to new tasks  Reconstructing and tracking an unknown object by exploiting interactive manipulation and multi-modal feedback  Multiple sensor fusion based adaptive grasping and manipulation control framework enhanced by human motor skills extractionTo represent human motor skills while interacting with objects/tools that differ in size, shape and stiffness, we will create an augmented hierarchical primitive-based library, with respect to human hand/arm stiffness, motion and finger gaiting. With online perception feedback, this primitives library will provide the knowledge basis for generalizable skills transferring from human to a robotic hand-arm system, by a hierarchical adaptive grasping and manipulation control method based on multi-modal sensor fusion. The skills generalization will be achieved regarding different object/tool sizes, shapes and stiffness. Object properties will be modelled online through visuo/tactile based exploration control method. Multi-modal perception feedback will also be used as input of the primitive library to generalize motion, stiffness, and gaiting trajectories. We will demonstrate the proposed grasping and manipulation approach with a typical daily-of-live task such as grasping a knife and cutting a fruit. Three institutes with a clear record in human motor skills learning (SCUT), visuo-tactile based recognition and interaction (UNIBI), and visuo-tactile based adaptive grasping and dexterous manipulation with multi-fingered robotic hands (DLR), will tightly cooperate towards this aim.

机器人学家们已经做出了巨大的努力来模仿人手，不仅从形式上，而且从功能上。然而，鲁棒地抓取和操纵未知物体/工具仍然是一个有待彻底解决的开放问题。在这个项目中，我们将研究一种基于人类运动技能提取的方法，以实现机器人手臂/手系统上的鲁棒灵巧抓取和手操作： 一个新的框架，增强动态运动基元DMPs嵌入感知信息的人类技能提取和推广到新的任务 基于交互操作和多模态反馈的未知目标重建与跟踪 基于多传感器融合的自适应抓取和操纵控制框架通过人类运动技能提取来增强为了在与大小、形状和刚度不同的物体/工具交互时表示人类运动技能，我们将创建一个增强的基于分层连续性的库，关于人手/手臂刚度、运动和手指步态。通过在线感知反馈，该基元库将为基于多模态传感器融合的分层自适应抓取和操作控制方法从人到机器人手臂系统的可推广技能传递提供知识基础。将针对不同的物体/工具尺寸、形状和刚度实现技能概括。 对象属性将通过基于视觉/触觉的探索控制方法在线建模。多模态感知反馈也将被用作原始库的输入，以概括运动、刚度和步态轨迹。我们将展示一个典型的日常生活任务，如抓刀和切水果的抓和操作方法。在人类运动技能学习（SCUT），基于视觉-触觉的识别和交互（UNIBI）以及基于视觉-触觉的自适应抓取和多指机器人手（DLR）灵巧操作方面有着明确记录的三个研究所将为此目标紧密合作。