Deep networks for predicting human intent with respect to objects

Authors:
Richard Kelley;Liesl Wigand;Brian Hamilton;Katie Browne;Monica Nicolescu;Mircea Nicolescu
Affiliations:
University of Nevada, Reno, NV, USA;University of Nevada, Reno, NV, USA;University of Nevada, Reno, NV, USA;University of Nevada, Reno, NV, USA;University of Nevada, Reno, NV, USA;University of Nevada, University of Nevada, NV, USA
Venue:
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Year:
2012

Citing 2
Cited 0

Understanding human intentions via hidden markov models in autonomous mobile robots

Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

The Journal of Machine Learning Research

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Abstract

Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results for our system's performance.