  Effective interaction modeling and behavior prediction of dynamic agents play
a significant role in interactive motion planning for autonomous robots.
Although existing methods have improved prediction accuracy, few research
efforts have been devoted to enhancing prediction model interpretability and
out-of-distribution (OOD) generalizability. This work addresses these two
challenging aspects by designing a variational auto-encoder framework that
integrates graph-based representations and time-sequence models to efficiently
capture spatio-temporal relations between interactive agents and predict their
dynamics. Our model infers dynamic interaction graphs in a latent space
augmented with interpretable edge features that characterize the interactions.
Moreover, we aim to enhance model interpretability and performance in OOD
scenarios by disentangling the latent space of edge features, thereby
strengthening model versatility and robustness. We validate our approach
through extensive experiments on both simulated and real-world datasets. The
results show superior performance compared to existing methods in modeling
spatio-temporal relations, motion prediction, and identifying time-invariant
latent features.
