Object permanence, which refers to the concept that objects continue to exist even when they are no longer perceivable through the senses, is a crucial aspect of human cognitive development. In this work, we seek to incorporate this understanding into interactive robots by proposing a set of assumptions and rules to represent object permanence in multi-object, multi-agent interactive scenarios. We integrate these rules into the particle filter, resulting in the Object Permanence Filter (OPF). For multi-object scenarios, we propose an ensemble of K interconnected OPFs, where each filter predicts plausible object tracks that are resilient to missing, noisy, and kinematically or dynamically infeasible measurements, thus bringing perceptional robustness. Through several interactive scenarios, we demonstrate that the proposed OPF approach provides robust tracking in human-robot interactive tasks agnostic to measurement type, even in the presence of prolonged and complete occlusion. Webpage: https://opfilter.github.io/.

翻译：目标持久性（Object Permanence）是指即使物体无法通过感官感知，其仍持续存在的认知概念，这是人类认知发展的关键组成部分。在本工作中，我们通过提出一组假设和规则来描述多对象、多代理交互场景中的目标持久性，并尝试将这一认知理解融入交互式机器人中。我们将这些规则集成到粒子滤波器中，从而提出目标持久性滤波器（Object Permanence Filter, OPF）。针对多对象场景，我们提出由K个相互连接的OPF组成的集成系统，每个滤波器能够预测合理的对象轨迹，这些轨迹对缺失、噪声以及运动学或动力学不可行的测量具有鲁棒性，从而增强了感知的稳健性。通过多个交互场景的实验，我们验证了所提出的OPF方法在人类-机器人交互任务中能够实现鲁棒跟踪，且不受测量类型限制，即使在长期且完全遮挡的情况下仍能保持性能。网页：https://opfilter.github.io/。