Abstract
The deployment of robot assistants in large indoor spaces has seen significant growth, with escorting tasks becoming a key application. However, most current escorting robots primarily rely on navigation-focused strategies, assuming that the person being escorted will follow without issue. In crowded environments, this assumption often falls short, as individuals may struggle to keep pace, become obstructed, get distracted, or need to stop unexpectedly. As a result, conventional robotic systems are often unable to provide effective escorting services due to their limited understanding of human movement dynamics. To address these challenges, an effective escorting robot must continuously detect and interpret human actions during the escorting process and adjust its movement accordingly. However, there is currently no existing dataset designed specifically for human action detection in the context of escorting. Given that escorting often occurs in crowded environments, where other individuals may enter the robot's camera view, the robot also needs to identify the specific human it is escorting (the subject) before predicting their actions. Since no existing model performs both person re-identification and action prediction in real-time, we propose a novel neural network architecture that can accomplish both tasks. This enables the robot to adjust its speed dynamically based on the escortee's movements and seamlessly resume escorting after any disruption. In comparative evaluations against strong baselines, our system demonstrates superior efficiency and effectiveness, showcasing its potential to significantly improve robotic escorting services in complex, real-world scenarios.
Abstract (translated)
在大型室内空间中部署机器人助手已经取得了显著的增长，其中引导任务已成为关键应用之一。然而，大多数现有的引导机器人主要依赖于导航策略，假设被引导的人能够顺利跟随而没有任何问题。但在拥挤的环境中，这一假设往往无法实现，因为个体可能会遇到跟不上步伐、被阻挡、分心或意外停下等问题。因此，传统的机器人系统由于对人类运动动态的理解有限，通常难以提供有效的引导服务。 为了解决这些问题，一个高效的引导机器人必须能够在引导过程中持续检测和解释人的动作，并根据这些信息调整自己的移动方式。然而，目前还没有专门针对引导场景设计的人类行为检测数据集。考虑到引导任务往往发生在拥挤的环境中，在这种情况下，其他个体可能会进入机器人的摄像机视野内，因此机器人需要在预测被引导人（目标）的行为之前识别出特定的这个人。由于现有的模型无法同时实现实时的人物再识别和动作预测，我们提出了一种新型神经网络架构来实现这两个任务。这使得机器人能够根据跟随者的移动情况动态调整速度，并且能够在任何干扰后无缝地继续进行引导。 在与强大基准方法进行比较评估中，我们的系统展示了更高的效率和有效性，表明它有潜力显著提高复杂现实场景中的机器人引导服务的水平。
URL
https://arxiv.org/abs/2506.23573
