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The ability of humans to quickly identify general concepts from a handful ofimages has proven difficult to emulate with robots. Recently, a computerarchitecture was developed that allows robots to mimic some aspects of thishuman ability by modeling concepts as cognitive programs using an instructionset of primitive cognitive functions. This allowed a robot to emulate humanimagination by simulating candidate programs in a world model beforegeneralizing to the physical world. However, this model used a naive searchalgorithm that required 30 minutes to discover a single concept, and becameintractable for programs with more than 20 instructions. To circumvent thisbottleneck, we present an algorithm that emulates the human cognitiveheuristics of object factorization and sub-goaling, allowing human-levelinference speed, improving accuracy, and making the output more explainable.

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs