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Creators/Authors contains: "Mooney, R."

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  1. ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform both clarification and active learning in the context of an interactive language-based image retrieval task motivated by an on-line shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions. 
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    Accepted Manuscript Full Text Available
  2. ; ; ( , Conference on Robot Learning)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ( , Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0))
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ( , Proceedings of the International Conference on Robotics and Automation (ICRA))
    Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically -- continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ( , Proceedings of the Eleventh International Conference on Social Robotics)
    null (Ed.)
    Full Text Available