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

By Ilker Yildirim, Tobias Gerstenberg, Basil Saeed, Marc Toussaint, Josh Tenenbaum. 2017

24 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Experimental and Cognitive Psychology

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Citations in the corpus

The limits and potentials of deep learning for robotics (2018, background): “…grounded in real world experience, are well suited for closed-loop control, and can be learned and Recent approaches to end-to-end training of deep networks forgo the use of explicit physics models, learning predictive models and controls from raw experiences [9], [16], [18], [36], [129], [132].”
Relational inductive bias for physical construction in humans and machines (2018, background): “However, while W. Li et al. (2017)’s approach is learning-based, it does not include a relational inductive bias; similarly, Yildirim et al. (2017)’s approach has a relational inductive bias, but no learning. ar X iv :1 80 6.”