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 one sense, annotation can be obtained without human supervision by automatically processing the data and extracting useful intelligence from the collected information. Metadata can be analyzed as a form of annotation for the payload it describes. Often, the input data itself comes from data sources rich with explicit or implicit human annotation. In @cite_63 , automatic classification methods are trained using existing human-interaction datasets that have been previously grouped by activity. While effective in its own right, this type of unsupervised learning needs to be validated before being put to used in any context where safety, preference, or informed judgment is required. One problem with unintentional annotation is that it is unstructured and noisy by nature, making the extraction of usable intelligence a considerably more difficult proposition @cite_62 . When possible, it is preferable to use annotations that are directly aimed at enhancing the understanding of a situation or task that one intends to refine.
@cite_62 @cite_63
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We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets. In natural language acquisition, it is dicult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, existing results for active learning have only considered standard classication tasks. To reduce annotation eort while maintaining accuracy, we apply active learning to two non-classication tasks in natural language processing: semantic parsing and information extraction. We show that active learning can signicantly reduce the number of annotated examples required to achieve a given level of performance for these complex tasks.