Multimodal real-time contingency detection for HRI

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 methods 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.