当前位置: X-MOL 学术arXiv.cs.HC › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Pose Imitation Constraints for Collaborative Robots
arXiv - CS - Human-Computer Interaction Pub Date : 2020-09-23 , DOI: arxiv-2009.10947
Glebys Gonzalez and Juan Wachs

Achieving human-like motion in robots has been a fundamental goal in many areas of robotics research. Inverse kinematic (IK) solvers have been explored as a solution to provide kinematic structures with anthropomorphic movements. In particular, numeric solvers based on geometry, such as FABRIK, have shown potential for producing human-like motion at a low computational cost. Nevertheless, these methods have shown limitations when solving for robot kinematic constraints. This work proposes a framework inspired by FABRIK for human pose imitation in real-time. The goal is to mitigate the problems of the original algorithm while retaining the resulting humanlike fluidity and low cost. We first propose a human constraint model for pose imitation. Then, we present a pose imitation algorithm (PIC), and it's soft version (PICs) that can successfully imitate human poses using the proposed constraint system. PIC was tested on two collaborative robots (Baxter and YuMi). Fifty human demonstrations were collected for a bi-manual assembly and an incision task. Then, two performance metrics were obtained for both robots: pose accuracy with respect to the human and the percentage of environment occlusion/obstruction. The performance of PIC and PICs was compared against the numerical solver baseline (FABRIK). The proposed algorithms achieve a higher pose accuracy than FABRIK for both tasks (25%-FABRIK, 53%-PICs, 58%-PICs). In addition, PIC and it's soft version achieve a lower percentage of occlusion during incision (10%-FABRIK, 4%-PICs, 9%-PICs). These results indicate that the PIC method can reproduce human poses and achieve key desired effects of human imitation.

中文翻译:

协作机器人的姿势模仿约束

在机器人中实现类人运动一直是机器人研究的许多领域的基本目标。反向运动 (IK) 求解器已被探索作为提供具有拟人运动的运动学结构的解决方案。特别是,基于几何的数值求解器,如 FABRIK,已经显示出以低计算成本产生类人运动的潜力。然而,这些方法在求解机器人运动学约束时显示出局限性。这项工作提出了一个受 FABRIK 启发的框架,用于实时模仿人体姿势。目标是减轻原始算法的问题,同时保留由此产生的人性化的流动性和低成本。我们首先提出了一种用于姿势模仿的人体约束模型。然后,我们提出了一种姿势模仿算法(PIC),它' s 软版本 (PIC),可以使用所提出的约束系统成功模仿人体姿势。PIC 在两个协作机器人(Baxter 和 YuMi)上进行了测试。收集了 50 个人工演示用于手动组装和切割任务。然后,为两个机器人获得了两个性能指标:相对于人类的姿势精度和​​环境遮挡/障碍物的百分比。PIC 和 PIC 的性能与数值求解器基线 (FABRIK) 进行了比较。所提出的算法在这两项任务(25%-FABRIK、53%-PICs、58%-PICs)上实现了比 FABRIK 更高的姿态精度。此外,PIC 及其软版本在切口期间实现了较低的闭塞百分比(10%-FABRIK、4%-PICs、9%-PICs)。
更新日期:2020-10-14
down
wechat
bug