Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.

Mobile robots and IoT (Internet of Things) devices can increase productivity, but only if they can be programmed by workers who understand the domain. This is especially true in manufacturing. Visual programming in the spatial context of the operating environment can enable mental models at a familiar level of abstraction. However, spatial-visual programming is still in its infancy; existing systems lack IoT integration and fundamental constructs, such as functions, that are essential for code reuse, encapsulation, or recursive algorithms. We present Vipo, a spatial-visual programming system for robot-IoT workflows. Vipo was designed with input from managers at six factories using mobile robots. Our user study (n=22) evaluated efficiency, correctness, comprehensibility of spatial-visual programming with functions.

Design contests and group creativity support systems have demonstrated the value of crowds for producing a solution to a design or engineering problem. However, when the goal is not one idea, but many ideas, an uncoordinated crowd effort would likely lead to a set of ideas clustered around those that are easiest to think of. We present BlueSky, a crowd-powered system that coordinates microtask workers to enumerate a uniform sample of textual ideas on a given topic. Through the process of soliciting ideas, an ontology is developed, which is used to categorize the ideas in the list. That categorization then reveals combinations of attributes that have not yet been covered. Our evaluation with four list topics compared BlueSky to freeform solicitation of ideas with respect to comprehensiveness (coverage over the entire idea space) and uniformity (mitigating the tendency to emphasize ideas that are easy to think of).