For smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and “robotic embodiment of learned assembly tasks in the form of skills.” In the former, robots observe human assembly operations to learn a sequence of assembly tasks, which is formalized into a human assembly script. The latter transforms the human assembly script into a robot assembly script in which a sequence of robot-executable assembly tasks are defined based on action planning supported by workspace modeling and simulated retargeting. The assembly tasks, in the form of the robot assembly script, are then implemented via pretrained robot skills. These skills aim to enable robots to execute difficult tasks that involve inherent uncertainties and variations. We validate the proposed approach by building a prototype of the automated robotic assembly system for a power breaker and an electronic set-top box. The results verify that the proposed automated robotic assembly system is not only feasible but also viable, as it is associated with a dramatic reduction in the human effort required for automating robotic assembly.

This demonstration introduces SiCi (Smart ideas for Creative interplay) that brings a single-body robot to life by delivering a variety of contents and then fosters children's creativity and innovation in education.

This paper introduces the r-learning (robot-learning) system that utilizes robotic interactions to personalize instructions for individual children. A child who is not familiar with using an educational tool to study could learn readily from educational contents through various interactions with a robot which is based on the behavior control. The contents which are chosen according to a child's learning data extracted by analysis from human-robot interaction data could lift the educational effectiveness. An r-learning scenario is designed by using Petri net to handle exception occurred in a learning process. The robot contents maker and the Open API for operating a robot are described as management utilities for a personalized r-learning system.