Data-driven methods have great advantages in modeling complicated human
behavioral dynamics and dealing with many human-robot interaction applications.
However, collecting massive and annotated real-world human datasets has been a
laborious task, especially for highly interactive scenarios. On the other hand,
algorithmic data generation methods are usually limited by their model
capacities, making them unable to offer realistic and diverse data needed by
various application users. In this work, we study trajectory-level data
generation for multi-human or human-robot interaction scenarios and propose a
learning-based automatic trajectory generation model, which we call Multi-Agent
TRajectory generation with dIverse conteXts (MATRIX). MATRIX is capable of
generating interactive human behaviors in realistic diverse contexts. We
achieve this goal by modeling the explicit and interpretable objectives so that
MATRIX can generate human motions based on diverse destinations and
heterogeneous behaviors. We carried out extensive comparison and ablation
studies to illustrate the effectiveness of our approach across various metrics.
We also presented experiments that demonstrate the capability of MATRIX to
serve as data augmentation for imitation-based motion planning.

通过研究多人或人机交互场景的轨迹级数据生成，我们提出了一种基于学习的自动轨迹生成模型 MATRIX，它能在多样化的真实背景中生成交互式的人类行为，并通过模拟规划为数据增强提供了实验验证。