面向交互分割与学习的过渡状态聚类
Transition State Clustering for Interaction Segmentation and Learning
AI总结：
针对人机交互分割中人机状态不匹配问题，提出在过渡边界状态上学习额外混合分布的分层方法，减少误分类并提升基于高斯混合方法的预测性能。
摘要：
具有底层高斯混合结构的隐马尔可夫模型已被证明能够通过高斯混合回归，从演示中有效学习各种交互任务中的人机交互。然而，当仅使用人的观测状态来分割交互时，与使用人和机器人的联合状态相比会出现不匹配。为了增强这种底层分割能力，并进而提升此类基于高斯混合方法的预测能力，我们采用一种分层方法，在过渡边界的状态上额外学习一个混合分布。这有助于防止通常在此类状态中出现的误分类。我们发现，我们的框架提升了底层基于高斯混合方法的性能，并在握手和碰拳等多种交互任务上对其进行了评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
