Dynamic Relational Inference in Multi-Agent Trajectories

TL;DR

This paper analyzes the limitations of neural relational inference (NRI) in multi-agent trajectory analysis, introduces an extended model DYARI for dynamic relations, and demonstrates its effectiveness on simulated and real-world data.

Contribution

It identifies NRI's limitations with short observations and proposes DYARI, a novel model for dynamic relational inference in multi-agent systems.

Findings

NRI's performance degrades with short observation sequences.

DYARI effectively infers changing relations in dynamic multi-agent systems.

The model performs well on both simulated physics data and real basketball trajectories.

Abstract

Inferring interactions from multi-agent trajectories has broad applications in physics, vision and robotics. Neural relational inference (NRI) is a deep generative model that can reason about relations in complex dynamics without supervision. In this paper, we take a careful look at this approach for relational inference in multi-agent trajectories. First, we discover that NRI can be fundamentally limited without sufficient long-term observations. Its ability to accurately infer interactions degrades drastically for short output sequences. Next, we consider a more general setting of relational inference when interactions are changing overtime. We propose an extension ofNRI, which we call the DYnamic multi-AgentRelational Inference (DYARI) model that can reason about dynamic relations. We conduct exhaustive experiments to study the effect of model architecture, under-lying dynamics and training scheme on the performance of dynamic relational inference using a simulated physics system. We also showcase the usage of our model on real-world multi-agent basketball trajectories.