发表机构
Tongji University; Shanghai Research Institute for Intelligent Autonomous Systems; University at Buffalo-SUNY（同济大学; 上海自主智能无人系统科学中心; 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
TRACER提出双向滚动时域框架，通过逐实体概率响应模型与持久信念更新，闭环预测与适应，提升多机器人社交导航的交互建模与无碰撞性能。
AI 中文摘要
在人类共享空间中的多机器人导航本质上是交互式的：协调的机器人运动会影响附近实体的响应方式，而这些响应又为后续的机器人决策提供了宝贵信息。然而，现有方法通常分别处理动作条件预测、多机器人规划或在线适应，因此缺乏统一的机制来建模机器人-实体联合交互并根据已执行的交互结果调整未来决策。为解决这一空白，我们提出了TRACER，一个双向滚动时域框架，它闭环了预测与适应之间的循环。TRACER使用一个逐实体概率响应模型来评估候选（即机器人团队可替代的可行未来运动计划）轨迹，该模型将单个机器人效应与非加性成对交互分离；在执行选定的轨迹前缀后，它利用同步观测到的响应更新关于潜在响应模式的持久身份绑定信念。这些更新的信念随后在概率安全性和响应感知成本标准下指导后续的候选评估。实验表明：（i）TRACER比容量匹配的加性预测器更准确地捕捉非加性多机器人交互效应，（ii）持久的身份一致证据改善了响应预测和下游重规划，（iii）完整的TRACER框架在SocialGym2多机器人社交导航基准上相对于独立机器人基线提高了无碰撞完成率。
英文摘要
Multi-robot navigation in human-shared spaces is inherently interactive: coordinated robot motions influence how nearby entities respond, while those responses provide valuable information for subsequent robot decisions. However, existing methods typically address action-conditioned prediction, multi-robot planning, or online adaptation separately, and therefore lack a unified mechanism for modeling joint robot-entity interactions and adapting future decisions from executed interaction outcomes. To address this gap, we propose TRACER, a bi-directional receding-horizon framework that closes the loop between prediction and adaptation. TRACER evaluates candidate (i.e., alternative feasible future motion plans for the robot team) trajectories using a per-entity probabilistic response model that separates individual-robot effects from non-additive pairwise interactions; after executing the selected trajectory prefix, it updates persistent identity-bound beliefs over latent response modes using the synchronized observed responses. These updated beliefs then guide subsequent candidate evaluation under probabilistic safety and response-aware cost criteria. Experiments show that (i) TRACER more accurately captures non-additive multi-robot interaction effects than a capacity-matched additive predictor, (ii) persistent identity-consistent evidence improves response prediction and downstream replanning, and (iii) the complete TRACER framework improves collision-free completion over an independent-robot baseline on the SocialGym2 multi-robot social-navigation benchmark.
Comments9 pages, 5 figures
