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An Interactive Framework for Learning Continuous Actions Policies Based on Corrective Feedback. J INTELL ROBOT SYST 2018. [DOI: 10.1007/s10846-018-0839-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Cuayáhuitl H, Frommberger L, Dethlefs N, Raux A, Marge M, Zender H. Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots. ACM T INTERACT INTEL 2014. [DOI: 10.1145/2670539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.
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Affiliation(s)
| | | | | | | | - Mathew Marge
- Carnegie Mellon University, United States of America
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