4 research outputs found
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Learning attributes of real-world objects by clustering multimodal sensory data
The goal of this work is to propose a framework for learning attributes of real-world objects via a clustering-based approach that aims to reduce the amount of human effort required in the form of labels for object categorization. Due to clustering, with just a single annotation, we can get information about all the objects in a cluster. In the field of robotics, even though studies have focused on the problem of object categorization, the aspect of the amount of workload for a user has not been explored much. However, as the presence of robots has started growing in our daily lives, it is important to reduce the human effort required in labelling for a robot to learn about its environment. Therefore, we propose a hierarchical clustering-based model that can learn the attributes of objects without any prior knowledge about them. It clusters multi-modal sensory data obtained by exploring real-world objects in an unsupervised fashion and then obtains labels for these clusters with the help of a human and uses this information to predict attributes of novel objects.Computer Science
BWIBots: A platform for bridging the gap between AI and human–robot interaction research
Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform
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Automatic risk factor detection for externalizing disorders from naturalistic audio
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