This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a Humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines.

翻译：本文提出了一种从人人交互中学习协调良好的人机交互的方法。我们设计了一种混合方法，将隐马尔可夫模型作为变分自编码器的潜在空间先验，以建模交互智能体之间的联合分布。通过利用从人人交互中学到的交互动力学，我们学习人机交互，并将基于人类观测的机器人运动条件生成融入训练过程，从而预测更准确的机器人轨迹。生成的机器人运动进一步通过逆运动学进行适配，以确保与人类保持所需的物理邻近性，从而结合了关节空间学习的简便性与任务空间可达性的精确度。对于接触密集型交互，我们利用隐马尔可夫模型分割来调节机器人刚度，以实现柔顺交互。通过在类人机器人上部署并进行用户研究，我们验证了所提方法的有效性。尽管仅基于两个人的数据进行训练，该方法仍能很好地泛化到不同人类用户。用户认为我们的方法更类人、更及时、更准确，并且与其他基线方法相比，更倾向于选择我们的方法。