Recurrent Slow Feature Analysis for Developing Object Permanence in Robots

In this work, we propose a biologically inspired framework for developing object permanence in robots. In particular, we build upon a previous work on a slowness principle-based visual model (Wiskott and Sejnowski, 2002), which was shown to be adept at tracking salient changes in the environment, while seamlessly “understanding” external causes, and self-emerging structures that resemble the human visual system. We propose an extension to this architecture with a prefrontal cortex-inspired recurrent loop that enables a simple short term memory, allowing the previously reactive system to retain information through time. We argue that object permanence in humans develop in a similar manner, that is, on top a previously matured object concept. Furthermore, we show that the resulting system displays the very behaviors which are thought to be cornerstones of object permanence understanding in humans. Specifically, the system is able to retain knowledge of a hidden object’s velocity, as well as identity, through (finite) occluded periods.