Object-Oriented Learning (OOL): Perception, Representation, and Reasoning

International Conference on Machine Learning (ICML)

Friday July 17, 2020, Virtual Workshop

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The ability to build a wide array of physical structures, from sand castles to skyscrapers, is a hallmark of human intelligence. What computational mechanisms enable humans to reason about how such structures are built? Here we conduct an empirical investigation of how people solve challenging physical assembly problems and update their policies across repeated attempts. Participants viewed silhouettes of 8 unique towers in a 2D virtual environment simulating rigid-body physics, and aimed to reconstruct each one using a fixed inventory of rectangular blocks. We found that people learned to build each target tower more accurately across repeated attempts, and that these gains reflect both group-level convergence upon a smaller set of viable policies, as well as error-dependent updating of each individual's policy. Taken together, our study provides a novel benchmark for evaluating how well algorithmic models of physical reasoning and planning correspond to human behavior.