MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu Rui Zhou Yida Yin Huidong Gao Masayoshi Tomizuka Jiachen Li
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.