用冗余的手臂和低复杂度的手进行类似人类的、特定任务的伸手和抓握

Minas Liarokapis, A. Dollar, K. Kyriakopoulos
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引用次数: 13

摘要

在本文中,我们提出了一种闭环,类人,特定任务的方法,具有冗余的机械臂和低复杂度的机械手。人类的示范是利用在示范中学习的方式,以映射人到类人机器人的运动。采用主成分分析(PCA)将类人机器人的运动变换为低维流形,并训练相应的导航函数(NF)模型。采用一系列抓取质量度量和任务兼容性指标来保证计算抓取结果的鲁棒性和目标机器人构型的任务专用性。最后的方案提供了拟人化的机器人运动,特定任务的机器人手臂配置和手抓取姿势,优化了指尖在物体表面的位置(从而实现鲁棒抓取),并保证收敛到期望的目标。物体的位置和几何形状被认为是先验已知的。通过不同机械手臂系统的仿真和实验验证了所提方法的有效性。该方案可用于各种人机交互(HRI)应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Humanlike, task-specific reaching and grasping with redundant arms and low-complexity hands
In this paper, we propose a methodology for closed-loop, humanlike, task-specific reaching and grasping with redundant robot arms and low-complexity robot hands. Human demonstrations are utilized in a learn by demonstration fashion, in order to map human to humanlike robot motion. Principal Components Analysis (PCA) is used to transform the humanlike robot motion in a low-dimensional manifold, where appropriate Navigation Function (NF) models are trained. A series of grasp quality measures, as well as task compatibility indexes are employed to guarantee robustness of the computed grasps and task specificity of goal robot configurations. The final scheme provides anthropomorphic robot motion, task-specific robot arm configurations and hand grasping postures, optimized fingertips placement on the object surface (that results to robust grasps) and guaranteed convergence to the desired goals. The position and geometry of the objects are considered a-priori known. The efficiency of the proposed methods is assessed with simulations and experiments that involve different robot arm hand systems. The proposed scheme can be useful for various Human Robot Interaction (HRI) applications.
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