Multimodal Real-Time Contingency Detection for HRI
Author(s)
Chu, Vivian
Bullard, Kalesha
Thomaz, Andrea L.
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Abstract
Our goal is to develop robots that naturally engage people in social exchanges. In this paper, we focus on the
problem of recognizing that a person is responsive to a robot’s
request for interaction. Inspired by human cognition, our approach is to treat this as a contingency detection problem. We present a simple discriminative Support Vector Machine
(SVM) classifier to compare against previous generative meth-
ods introduced in prior work by Lee et al. [1]. We evaluate
these methods in two ways. First, by training three separate
SVMs with multi-modal sensory input on a set of batch data
collected in a controlled setting, where we obtain an average F₁ score of 0.82. Second, in an open-ended experiment setting with seven participants, we show that our model is able to perform contingency detection in real-time and generalize to new people
with a best F₁ score of 0.72.
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Date
2014-09
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