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Situated Bayesian Reasoning Framework for Robots Operating in Diverse Everyday Environments

  • Sonia ChernovaEmail author
  • Vivian Chu
  • Angel Daruna
  • Haley Garrison
  • Meera Hahn
  • Priyanka Khante
  • Weiyu Liu
  • Andrea Thomaz
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

General-purpose robots operating in unstructured environments have the potential to benefit by leveraging abstract, commonsense knowledge for task execution. In this paper, we present an approach for automatically generating a compact semantic knowledge base, relevant to a robot’s particular operating environment, given only a small number of object labels obtained from object recognition or a robot’s task description. In order to cope with noise and non-deterministic data across our data sources, we formulate our representation as a statistical relational model represented as a Baysian Logic Network. We validate our approach in both abstract and real-world domains, demonstrating the robot’s ability to perform inference about object categories, locations and properties given a small amount of local information. Additionally, we present an approach for interactively validating the mined information with the help of a co-located user.

Notes

Acknowledgements

This work is supported in part by NSF IIS 1564080 and ONR N000141612835.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sonia Chernova
    • 1
    Email author
  • Vivian Chu
    • 1
  • Angel Daruna
    • 1
  • Haley Garrison
    • 1
  • Meera Hahn
    • 1
  • Priyanka Khante
    • 2
  • Weiyu Liu
    • 1
  • Andrea Thomaz
    • 2
  1. 1.Institute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Electrical and Computer EngineeringThe University of Texas at AustinAustinUSA

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