发表机构
Guohao School, Tongji University; Shanghai Research Institute for Intelligent Autonomous Systems; Tongji University; Western University; Aeromind Technology Co., Ltd.; University at Buffalo-SUNY（同济大学国豪书院; 上海智能无人系统科学中心; 同济大学; 西安大略大学; 深圳智元科技有限公司; 纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对开放人机环境中异构多智能体导航问题，提出SAGE，通过有向异构图和异构图变换器编码交互，扩散生成模块建模轨迹，无训练安全-社会能量引导机制优化轨迹，实验验证其有效性及可扩展性。
AI 中文摘要
在开放的人机环境中实现安全且符合社会规范的导航，要求机器人对具有不同动态、自主水平和社会角色的异构参与者进行推理。现有轨迹预测和规划方法存在局限性。本文提出SAGE，将机器人和周围实体表示为有向异构图，用异构图变换器编码特定类型的不对称交互。基于此，扩散生成模块联合建模未来实体轨迹和机器人轨迹计划。推理时，无训练的安全-社会能量引导机制优化采样轨迹。实验验证了SAGE在提高安全性和社会合规性同时保持任务性能的有效性，展示了其作为复杂环境中社会感知多智能体导航可扩展框架的潜力。
英文摘要
Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and social roles. Existing trajectory prediction and planning methods often rely on homogeneous interaction assumptions or enforce only geometric collision constraints, making it difficult to jointly model asymmetric interactions, coupled prediction-planning, and soft social norms. This paper proposes SAGE, a socially-aware generative engine for heterogeneous multi-agent navigation. SAGE represents robots and surrounding entities as a directed heterogeneous graph and employs a Heterogeneous Graph Transformer (HGT) to encode type-specific asymmetric interactions. Conditioned on the resulting context, a diffusion-based generative module jointly models future entity trajectories and robot trajectory plans. During inference, a training-free safety-social energy guidance mechanism refines sampled robot trajectories using differentiable collision, kinematic, task-progress, and role-conditioned social-compliance terms. Extensive experiments on real-world (ETH/UCY and SDD) and synthetic datasets verify the effectiveness of SAGE in improving safety and social compliance while maintaining task performance. The proposed guidance mechanism consistently reduces collision and social-violation rates, scales to teams of up to 20 robots, and enables explicit control of the safety-accuracy-task trade-off without retraining. These findings demonstrate the potential of SAGE as a scalable framework for socially-aware multi-agent navigation in complex environments.
Comments16 pages, 5 figures, and 14 tables. Includes supplementary experimental details
