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.
On the other hand, there has been a lot of effort in developing methods for reducing the cognitive load on the user in 2D-GUIs. In @cite_77 , simple constraining of motions was effective in offloading some of the cognitive effort. The familiarity and widespread use of smartphones and tablets has also brought attention to touch-based control. In @cite_78 , a simple touch-based interface was aimed at reducing operator fatigue.
@cite_77 @cite_78
2592582799 2017919391
The interface is the main mechanism of communication between user and system features. In educational software, successful user interface designs minimize the cognitive load on users, thereby users can direct their efforts to maximize their understanding of the educational concepts being presented. We investigated whether a reduced interface make few cognitive demands on users in comparison to a complete interface. In this context, this research aims at analyzing a reduced and a complete interface of an interactive geometry software, and verify the educational benefits they provide. To this end, we designed the interfaces and carried out an experiment involving 69 undergraduate students. The experimental results indicate that an interface that hides advanced and extraneous features helps novice users to perform slightly better than novice users using a complete interface. After receiving proper training, however, a complete interface makes users more productive than a reduced interface. Touch-based verification --- the use of touch gestures (e.g., swiping, zooming, etc.) to authenticate users of touch screen devices --- has recently been widely evaluated for its potential to serve as a second layer of defense to the PIN lock mechanism. In all performance evaluations of touch-based authentication systems however, researchers have assumed naive (zero-effort) forgeries in which the attacker makes no effort to mimic a given gesture pattern. In this paper we demonstrate that a simple "Lego" robot driven by input gleaned from general population swiping statistics can generate forgeries that achieve alarmingly high penetration rates against touch-based authentication systems. Using the best classification algorithms in touch-based authentication, we rigorously explore the effect of the attack, finding that it increases the Equal Error Rates of the classifiers by between 339 and 1004 depending on parameters such as the failure-to-enroll threshold and the type of touch stroke generated by the robot. The paper calls into question the zero-effort impostor testing approach used to benchmark the performance of touch-based authentication systems.