軌道予測のためのマルチモーダル深層生成モデル：条件付き変分オートエンコーダによるアプローチ
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
人間の過去の行動とロボットの将来行動を条件として、将来の人間軌道のマルチモーダル確率分布を生成するCVAEベースの人間行動予測手法をチュートリアル形式で解説した論文。
著者: Boris Ivanovic, Karen Leung, Edward Schmerling, Marco Pavone
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
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.
