Active physical inference via reinforcement learning

Shuaiji Li, Yu Sun, Sijia Liu, Tianyu Wang, Todd M. Gureckis, Neil R. Bramley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When encountering unfamiliar physical objects, children and adults often perform structured interrogatory actions such as grasping and prodding, so revealing latent physical properties such as masses and textures. However, the processes driving and supporting these curious behaviors are still largely mysterious. In this paper, we develop and train an agent able to actively uncover latent physical properties such as the mass and force of objects in a simulated physical “micro-world'. Concretely, we used a simulation-based-inference framework to quantify the physical information produced by observation and interaction with the evolving dynamic environment. We used model-free reinforcement learning algorithm to train an agent to implement general strategies for revealing latent physical properties. We compare the behaviors of this agent to the human behaviors observed in a similar task.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st Annual Meeting of the Cognitive Science Society
Subtitle of host publicationCreativity + Cognition + Computation, CogSci 2019
PublisherThe Cognitive Science Society
Pages2126-2132
Number of pages7
ISBN (Electronic)0991196775, 9780991196777
StatePublished - 2019
Event41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 - Montreal, Canada
Duration: Jul 24 2019Jul 27 2019

Publication series

NameProceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019

Conference

Conference41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
Country/TerritoryCanada
CityMontreal
Period7/24/197/27/19

Keywords

  • active learning
  • physical simulation
  • probabilistic inference
  • reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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