Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, modeling complex interaction dynamics and capturing the possibility of many possible outcomes in such interactive settings is very challenging, which has recently prompted the study of several different approaches. In this work, we provide a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability distribution over future human trajectories conditioned on past interactions and candidate robot future actions. Specifically, the goals of this tutorial paper are to review and build a taxonomy of state-of-the-art methods in human behavior prediction, from physics-based to purely data-driven methods, provide a rigorous yet easily accessible description of a data-driven, CVAE-based approach, highlight important design characteristics that make this an attractive model to use in the context of model-based planning for human-robot interactions, and provide important design considerations when using this class of models.

中文翻译：

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用于轨迹预测的多模态深度生成模型：一种条件变分自动编码器方法

人类行为预测模型使机器人能够预测人类对其行为的反应，因此有助于设计安全和主动的机器人规划算法。然而，在这种交互环境中对复杂的交互动态建模并捕捉许多可能结果的可能性是非常具有挑战性的，这促使最近研究了几种不同的方法。在这项工作中，我们提供了一个关于人类行为预测的条件变分自动编码器 (CVAE) 方法的独立教程，该方法的核心是可以在以过去的交互和候选机器人未来动作为条件的未来人类轨迹上产生多模态概率分布。具体而言，本教程论文的目标是审查和构建人类行为预测中最先进方法的分类法，