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Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture
Abstract : In this paper we present a multi-modal human robot interaction architecture which is able to combine information coming from different sensory inputs, and can generate feedback for the user which helps to teach him/her implicitly how to interact with the robot. The system combines vision, speech and language with inference and feedback. The system environment consists of a Nao robot which has to learn objects situated on a table only by understanding absolute and relative object locations uttered by the user and afterwards points on a desired object to show what it has learned. The results of a user study and performance test show the usefulness of the feedback produced by the system and also justify the usage of the system in a real-world applications, as its classification accuracy of multi-modal input is around 80.8%. In the experiments, the system was able to detect inconsistent input coming from different sensory modules in all cases and could generate useful feedback for the user from this information.
Cited literature [14 references]
https://hal.inria.fr/hal-01417706
Contributor : Xavier Hinaut <>
Submitted on : Thursday, December 15, 2016 - 7:21:50 PM
Last modification on : Sunday, November 22, 2020 - 12:34:06 PM
Contributor : Xavier Hinaut <>
Submitted on : Thursday, December 15, 2016 - 7:21:50 PM
Last modification on : Sunday, November 22, 2020 - 12:34:06 PM
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