Learning models for shared control of human-machine systems with unknown dynamics

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

7 Scopus citations

Abstract

We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-theloop systems further in the discussion.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XIII, RSS 2017
EditorsNancy Amato, Siddhartha Srinivasa, Nora Ayanian, Scott Kuindersma
PublisherMIT Press Journals
ISBN (Electronic)9780992374730
DOIs
StatePublished - 2017
Event2017 Robotics: Science and Systems, RSS 2017 - Cambridge, United States
Duration: Jul 12 2017Jul 16 2017

Publication series

NameRobotics: Science and Systems
Volume13
ISSN (Electronic)2330-765X

Other

Other2017 Robotics: Science and Systems, RSS 2017
CountryUnited States
CityCambridge
Period7/12/177/16/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Learning models for shared control of human-machine systems with unknown dynamics'. Together they form a unique fingerprint.

  • Cite this

    Broad, A., Murphey, T. D., & Argall, B. D. (2017). Learning models for shared control of human-machine systems with unknown dynamics. In N. Amato, S. Srinivasa, N. Ayanian, & S. Kuindersma (Eds.), Robotics: Science and Systems XIII, RSS 2017 (Robotics: Science and Systems; Vol. 13). MIT Press Journals. https://doi.org/10.15607/rss.2017.xiii.037