Robotic Scene Understanding by Using a Dictionary Scene understanding is a fundamental task for intelligent robots, especially in humanrobot interaction. It is challenging due to the complexity of the human environment. In this paper, the proposed method integrates semantic analysis with object detection that enables robots to perceive scenes and deeply understands working environments. The model can extract deterministic entities of objects by analyzing their dictionary definitions. Therefore, robots can understand a scene at the objectlevel. These deterministic entities include the category, function, property, and composition of objects, and they can be used to generate feedback on how much robots can understand a scene by describing it in natural language. The feedback on how well robots understand the working space is an essential aspect to eliminate confusion during humanrobot interactions. The experiment part of this paper discussed the applicability of the proposed method on robots.

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.