Robotic arms are highly common in various automation processes such as manufacturing lines. However, these highly capable robots are usually degraded to simple repetitive tasks such as pick-and-place. On the other hand, designing an optimal robot for one specific task consumes large resources of engineering time and costs. In this paper, we propose a novel concept for optimizing the fitness of a robotic arm to perform a specific task based on human demonstration. Fitness of a robot arm is a measure of its ability to follow recorded human arm and hand paths. The optimization is conducted using a modified variant of the Particle Swarm Optimization for the robot design problem. In the proposed approach, we generate an optimal robot design along with the required path to complete the task. The approach could reduce the time-to-market of robotic arms and enable the standardization of modular robotic parts. Novice users could easily apply a minimal robot arm to various tasks. Two test cases of common manufacturing tasks are presented yielding optimal designs and reduced computational effort by up to 92%.

翻译：机械臂在制造流水线等各类自动化过程中极为常见，然而这些高性能机器人通常被降级为简单的重复性任务（如拾取与放置）。另一方面，为特定任务设计最优机器人需要耗费大量工程时间与成本。本文提出一种基于人类示教优化机械臂任务适配性的新概念——机械臂适配性指其跟随记录的人类手臂与手部路径的能力。优化过程采用改进型粒子群优化算法解决机器人设计问题。在所提方法中，我们生成最优机器人设计的同时生成完成任务所需的路径。该方法可缩短机械臂的上市周期，并推动模块化机器人部件的标准化进程。普通用户可轻松将最小构型机械臂应用于多种任务。通过两个典型制造任务的测试案例，我们获得了最优设计方案，并将计算开销降低高达92%。