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
2040557557 2124994029 2276146857 2554839354
Crowdsourcing is the de-facto standard for gathering annotated data. While, in theory, data annotation tasks are assumed to be attempted by workers independently, in practice, data annotation tasks are often grouped into batches to be presented and annotated by workers together, in order to save on the time or cost overhead of providing instructions or necessary background. Thus, even though independence is usually assumed between annotations on data items within the same batch, in most cases, a worker's judgment on a data item can still be affected by other data items within the batch, leading to additional errors in collected labels. In this paper, we study the data annotation bias when data items are presented as batches to be judged by workers simultaneously. We propose a novel worker model to characterize the annotating behavior on data batches, and present how to train the worker model on annotation data sets. We also present a debiasing technique to remove the effect of such annotation bias from adversely affecting the accuracy of labels obtained. Our experimental results on synthetic and real-world data sets demonstrate that our proposed method can achieve up to +57 improvement in F1-score compared to the standard majority voting baseline. Detecting and correcting low quality submissions in crowdsourcing tasks is an important challenge. Prior work has primarily focused on worker outcomes or reputation, using approaches such as agreement across workers or with a gold standard to evaluate quality. We propose an alternative and complementary technique that focuses on the way workers work rather than the products they produce. Our technique captures behavioral traces from online crowd workers and uses them to predict outcome measures such quality, errors, and the likelihood of cheating. We evaluate the effectiveness of the approach across three contexts including classification, generation, and comprehension tasks. The results indicate that we can build predictive models of task performance based on behavioral traces alone, and that these models generalize to related tasks. Finally, we discuss limitations and extensions of the approach. Crowdsourcing has attracted significant attention from the database community in recent years and several crowdsourced databases have been proposed to incorporate human power into traditional database systems. One big issue in crowdsourcing is to achieve high quality because workers may return incorrect answers. A typical solution to address this problem is to assign each question to multiple workers and combine workers’ answers to generate the final result. One big challenge arising in this strategy is to infer worker’s quality. Existing methods usually assume each worker has a fixed quality and compute the quality using qualification tests or historical performance. However these methods cannot accurately estimate a worker’s quality. To address this problem, we propose a worker model and devise an incremental inference strategy to accurately compute the workers’ quality. We also propose a question model and develop two efficient strategies to combine the worker’s model to compute the question’s result. We implement our method and compare with existing inference approaches on real crowdsourcing platforms using real-world datasets, and the experiments indicate that our method achieves high accuracy and outperforms existing approaches. Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous “information pieceworkers”, have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all crowdsourcers must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in some way such as majority voting. In this paper, we consider a model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, based on low-rank matrix approximation, significantly outperforms majority voting and, in fact, is order-optimal through comparison to an oracle that knows the reliability of every worker.