Grounding Language Attributes to Objects using Bayesian Eigenobjects We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase. Our system is designed to learn from only a small amount of humanlabeled language data and generalize to viewpoints not represented in the languageannotated depth image training set. By decoupling 3D shape representation from language representation, this method is able to ground language to novel objects using a small amount of languageannotated depthdata and a larger corpus of unlabeled 3D object meshes, even when these objects are partially observed from unusual viewpoints. Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions. Our method also enables viewpoint transfer; trained on humanannotated data on a small set of depth images captured from frontal viewpoints, our system successfully predicted object attributes from rear views despite having no such depth images in its training set. Finally, we demonstrate our approach on a Baxter robot, enabling it to pick specific objects based on humanprovided natural language descriptions.

Look Further to Recognize Better: Learning Shared Topics and CategorySpecific Dictionaries for OpenEnded 3D Object Recognition Service robots are expected to operate effectively in humancentric environments for long periods of time. In such realistic scenarios, finegrained object categorization is as important as basiclevel object categorization. We tackle this problem by proposing an openended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and categoryspecific dictionaries. The general topics encode the common patterns of all categories, while the categoryspecific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous stateoftheart approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a realworld setting. Regarding computation time, the best result was obtained with a BagofWords method followed by a variant of the Latent Dirichlet Allocation approach.