  Reliable perception is essential for robots that interact with the world. But
sensors alone are often insufficient to provide this capability, and they are
prone to errors due to various conditions in the environment. Furthermore,
there is a need for robots to maintain a model of its surroundings even when
objects go out of view and are no longer visible. This requires anchoring
perceptual information onto symbols that represent the objects in the
environment. In this paper, we present a model for action-aware perceptual
anchoring that enables robots to track objects in a persistent manner. Our
rule-based approach considers inductive biases to perform high-level reasoning
over the results from low-level object detection, and it improves the robot's
perceptual capability for complex tasks. We evaluate our model against existing
baseline models for object permanence and show that it outperforms these on a
snitch localisation task using a dataset of 1,371 videos. We also integrate our
action-aware perceptual anchoring in the context of a cognitive architecture
and demonstrate its benefits in a realistic gearbox assembly task on a
Universal Robot.
