Relational inductive bias for physical construction in humans and machines

Understand

While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.

• We hypothesize that what these systems lack is a "relational inductive bias": a capacity for reasoning about inter-object relations and making choices over a structured description of a scene.
• To test this hypothesis, we focus on a task that involves gluing pairs of blocks together to stabilize a tower, and quantify how well humans perform.
• We then introduce a deep reinforcement learning agent which uses object- and relation-centric scene and policy representations and apply it to the task.