English

Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints

Artificial Intelligence 2017-07-27 v1 Robotics Machine Learning

Abstract

In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy.

Keywords

Cite

@article{arxiv.1707.08212,
  title  = {Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints},
  author = {Ilker Yildirim and Tobias Gerstenberg and Basil Saeed and Marc Toussaint and Josh Tenenbaum},
  journal= {arXiv preprint arXiv:1707.08212},
  year   = {2017}
}
R2 v1 2026-06-22T20:57:27.063Z