  Understanding objects is a central building block of artificial intelligence,
especially for embodied AI. Even though object recognition excels with deep
learning, current machines still struggle to learn higher-level knowledge,
e.g., what attributes an object has, and what can we do with an object. In this
work, we propose a challenging Object Concept Learning (OCL) task to push the
envelope of object understanding. It requires machines to reason out object
affordances and simultaneously give the reason: what attributes make an object
possesses these affordances. To support OCL, we build a densely annotated
knowledge base including extensive labels for three levels of object concept
(category, attribute, affordance), and the causal relations of three levels. By
analyzing the causal structure of OCL, we present a baseline, Object Concept
Reasoning Network (OCRN). It leverages causal intervention and concept
instantiation to infer the three levels following their causal relations. In
experiments, OCRN effectively infers the object knowledge while following the
causalities well. Our data and code are available at https://mvig-rhos.com/ocl.
