  Hidden Markov Models with an underlying Mixture of Gaussian structure have
proven effective in learning Human-Robot Interactions from demonstrations for
various interactive tasks via Gaussian Mixture Regression. However, a mismatch
occurs when segmenting the interaction using only the observed state of the
human compared to the joint state of the human and the robot. To enhance this
underlying segmentation and subsequently the predictive abilities of such
Gaussian Mixture-based approaches, we take a hierarchical approach by learning
an additional mixture distribution on the states at the transition boundary.
This helps prevent misclassifications that usually occur in such states. We
find that our framework improves the performance of the underlying Gaussian
Mixture-based approach, which we evaluate on various interactive tasks such as
handshaking and fistbumps.
