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.

本文介绍一个从人与人互动学习到人机互动的方法，通过使用隐马尔可夫模型作为潜在空间先验，并结合变分自编码器来建模互动代理之间的联合分布。利用从人与人互动中学习到的互动动力学来学习人机互动，并将人类观测结果作为训练基础，从而预测更准确的机器人轨迹。进一步通过逆运动学调整生成的机器人动作，以确保与人之间的期望物理接近性，结合了关节空间学习的便利性和准确的任务空间可达性。在富含接触的互动中，我们利用 HMM 分割来调节机器人的刚度以实现柔顺的互动。通过用户评测，我们验证了所提方法在一个人形机器人上的有效性。尽管仅通过两个人的数据进行训练，我们的方法在不同人类之间表现出很好的泛化性能。实验结果表明，用户认为我们的方法更具人性化、及时性和准确性，并且较其他基准方法更具优势。