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.

翻译：数据驱动方法在建模复杂人类行为动态及处理多人机交互应用方面具有显著优势。然而，大规模标注的真实世界人类数据集采集过程历来繁琐耗时，尤其针对高度交互场景；另一方面，算法自动生成方法受限于模型容量，难以提供各类应用场景所需的高保真度与多样性数据。本研究聚焦多人及人机交互场景下的轨迹级数据生成问题，提出基于学习的自动化轨迹生成模型——多智能体多样化场景轨迹生成模型（MATRIX）。该模型能在真实多样化场景中生成交互式人类行为。通过建立显式可解释的目标函数，MATRIX可依据不同目的地与异质行为特征生成人类运动轨迹。我们开展了全面的对比实验与消融研究，从多维度验证了本方法的有效性。此外，通过实验证明了MATRIX作为基于模仿的运动规划数据增强工具的潜力。