@article {5077, title = {Meta-strategy learning in physical problem solving: the effect of embodied experience}, journal = {bioRxiv}, year = {2021}, month = {08/2021}, abstract = {

Embodied cognition suggests that our experience in our bodies -- including our motor experiences -- shape our cognitive and perceptual capabilities broadly. Much work has studied how differences in the physical body (either natural or manipulated) can impact peoples cognitive and perceptual capacities, but often these judgments relate directly to those body differences. Here we focus instead on how natural embodied experience affects what kinds of abstract physical problem-solving strategies people use in a virtual task. We compare how groups with different embodied experience -- children and adults with congenital limb differences versus those born with two hands -- perform on this task, and find that while there is no difference in overall accuracy or time to complete the task, the groups use different meta-strategies to come to solutions. Specifically, both children and adults born with limb differences take a longer time to think before acting, and as a result take fewer overall actions to reach solutions to physical reasoning problems. Conversely, the process of development affects the particular actions children use as they age regardless of how many hands they were born with, as well as their persistence with their current strategy. Taken together, our findings suggest that differences in embodied experience drive the acquisition of different meta-strategies for balancing acting with thinking, deciding what kinds of actions to try, and deciding how persistent to be with a current action plan.

}, author = {Kelsey Allen and Kevin A Smith and Laura Bird and Joshua B. Tenenbaum and Tamar Makin and Dorothy Cowie} } @article {4683, title = {Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning}, journal = {Proceedings of the National Academy of Sciences}, year = {2020}, month = {11/2021}, pages = {201912341}, abstract = {

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use{\textemdash}using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the {\textquotedblleft}sample, simulate, update{\textquotedblright} (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.

}, keywords = {intuitive physics, physical problem solving, tool use}, issn = {0027-8424}, doi = {10.1073/pnas.1912341117}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1912341117}, author = {Kelsey Allen and Kevin A Smith and Joshua B. Tenenbaum} } @proceedings {4179, title = {Differentiable physics and stable modes for tool-use and manipulation planning}, year = {2018}, month = {06/2018}, abstract = {

We consider the problem of sequential manipulation and tool-use planning in domains that include physical interactions such as hitting and throwing. The approach integrates a Task And Motion Planning formulation with primitives that either impose stable kinematic constraints or differentiable dynamical and impulse exchange constraints at the path optimization level. We demonstrate our approach on a variety of physical puzzles that involve tool use and dynamic interactions. We then compare manipulation sequences generated by our approach to human actions on analogous tasks, suggesting future directions and illuminating current limitations.

}, author = {Marc Toussaint and Kelsey Allen and Kevin A Smith and Joshua B. Tenenbaum} } @article {4181, title = {End-to-end differentiable physics for learning and control}, year = {2018}, author = {Filipe de Avila Belbute-Peres and Kevin A Smith and Kelsey Allen and Joshua B. Tenenbaum and Zico Kolter} } @article {4182, title = {Relational inductive bias for physical construction in humans and machines}, year = {2018}, month = {06/2018}, abstract = {

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. Our results show that these structured representations allow the agent to outperform both humans and more naive approaches, suggesting that relational inductive bias is an important component in solving structured reasoning problems and for building more intelligent, flexible machines.

}, author = {Jessica B. Hamrick and Kelsey Allen and Victor Bapst and Tina Zhu and Kevin R. McKee and Joshua B. Tenenbaum and Battaglia, Peter} } @conference {2600, title = {Integrating Identification and Perception: A case study of familiar and unfamiliar face processing}, booktitle = {Proceedings of the Thirty-Eight Annual Conference of the Cognitive Science Society}, year = {2016}, month = {2016}, author = {Kelsey Allen and Ilker Yildirim and Joshua B. Tenenbaum} }