Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
Jiachen Li Chuanbo Hua Hengbo Ma Jinkyoo Park Victoria Dax Mykel J. Kochenderfer
Social robot navigation can be helpful in various contexts of daily life but
requires safe human-robot interactions and efficient trajectory planning. While
modeling pairwise relations has been widely studied in multi-agent interacting
systems, the ability to capture larger-scale group-wise activities is limited.
In this paper, we propose a systematic relational reasoning approach with
explicit inference of the underlying dynamically evolving relational
structures, and we demonstrate its effectiveness for multi-agent trajectory
prediction and social robot navigation. In addition to the edges between pairs
of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect
multiple nodes to enable group-wise reasoning in an unsupervised manner. Our
approach infers dynamically evolving relation graphs and hypergraphs to capture
the evolution of relations, which the trajectory predictor employs to generate
future states. Meanwhile, we propose to regularize the sharpness and sparsity
of the learned relations and the smoothness of the relation evolution, which
proves to enhance training stability and model performance. The proposed
approach is validated on synthetic crowd simulations and real-world benchmark
datasets. Experiments demonstrate that the approach infers reasonable relations
and achieves state-of-the-art prediction performance. In addition, we present a
deep reinforcement learning (DRL) framework for social robot navigation, which
incorporates relational reasoning and trajectory prediction systematically. In
a group-based crowd simulation, our method outperforms the strongest baseline
by a significant margin in terms of safety, efficiency, and social compliance
in dense, interactive scenarios.