Object Permanence Filter for Robust Tracking with Interactive Robots
arxiv(2024)
摘要
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/.
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