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.

翻译：社交机器人导航在日常生活的各种场景中都可能发挥作用，但需要安全的人机交互和高效的轨迹规划。尽管成对关系建模在多智能体交互系统中已被广泛研究，但捕捉更大范围群体活动的能能力有限。在本文中，我们提出了一种系统性的关系推理方法，显式地推断底层动态演化关系结构，并展示了其在多智能体轨迹预测和社交机器人导航中的有效性。除了节点对（即智能体）之间的边，我们提出以无监督方式推断自适应连接多个节点的超边，从而实现群体推理。我们的方法推断动态演化的关系图和超图以捕捉关系的演化，轨迹预测器利用这些图来生成未来状态。同时，我们提出对所学关系的尖锐性和稀疏性，以及关系演化的平滑性进行正则化，这被证明能增强训练稳定性和模型性能。所提出的方法在合成人群模拟和真实世界基准数据集上得到了验证。实验表明，该方法能推断出合理的关系，并实现了最先进的预测性能。此外，我们提出了一个用于社交机器人导航的深度强化学习（DRL）框架，该框架将关系推理和轨迹预测系统地整合在一起。在基于群体的人群模拟中，我们的方法在密集、交互场景下的安全性、效率和社会合规性方面显著优于最强基线。