Control proprioception for robust autonomous systems

Eric Homan, John Sustersic

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


This paper show how neural networks, configured for regression, can be used to learn the relationships between Inertial Motion Unit (IMU) data collected on a robotic platform and the robot's commanded system state. By learning how the IMU data relates to commanded robot state we can use the neural network to predict what commands have been issued to the robot. By comparing the prediction to the actual commands we can determine if the perceived behavior of our robot matches the commanded behavior. This enables the vehicle to identify issues with control and potentially take corrective actions needed to enable long-duration autonomy.

Original languageEnglish (US)
Title of host publicationOCEANS 2017 � Anchorage
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9780692946909
StatePublished - Dec 19 2017
EventOCEANS 2017 - Anchorage - Anchorage, United States
Duration: Sep 18 2017Sep 21 2017

Publication series

NameOCEANS 2017 - Anchorage


OtherOCEANS 2017 - Anchorage
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Automotive Engineering
  • Water Science and Technology
  • Acoustics and Ultrasonics
  • Instrumentation
  • Ocean Engineering


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