We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
In the work of @cite_56 , several tasks and trials were tested using crowdsourcing. Here, the annotation tasks were simple in order to allow testing the approach, and later integrated with the ultimate objective of completing pick-and-place tasks. They found that in many cases a single average worker'' produced poor results, but that by cleaning and averaging @math of them, high accuracy was obtained.
@cite_56
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Crowdsourcing complex creative tasks remains difficult, in part because these tasks require skilled workers. Most crowdsourcing platforms do not help workers acquire the skills necessary to accomplish complex creative tasks. In this paper, we describe a platform that combines learning and crowdsourcing to benefit both the workers and the requesters. Workers gain new skills through interactive step-by-step tutorials and test their knowledge by improving real-world images submitted by requesters. In a series of three deployments spanning two years, we varied the design of our platform to enhance the learning experience and improve the quality of the crowd work. We tested our approach in the context of LevelUp for Photoshop, which teaches people how to do basic photograph improvement tasks using Adobe Photoshop. We found that by using our system workers gained new skills and produced high-quality edits for requested images, even if they had little prior experience editing images.