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.

翻译：基于混合高斯隐马尔可夫模型的方法通过高斯混合回归在从示教中学习人机交互任务方面已被证明有效。然而，仅利用人类观测状态进行交互分割与利用人机联合状态进行分割之间存在失配问题。为改进此类基于高斯混合方法的基础分割能力及其预测性能，我们提出层次化方法——在过渡边界上学习额外的状态混合分布。这有助于防止此类状态下常见的误分类问题。结果表明，我们的框架提升了基础高斯混合方法的性能，我们在握手、碰拳等多种交互任务上进行了评估验证。