Discrete N-Dimensional Entropy of Behavior: DNDEB

Michael Young, Mahdieh Nejati Javaremi, Brenna D. Argall

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

1 Scopus citations


Shared control for human-robot teams - where both the human and the robot's autonomy provide commands to the hardware - offers advantages over fully teleoperated or fully autonomous systems by utilizing the unique skill sets of both the human and robot's autonomy simultaneously. However, the mechanism by which control is shared is often static and many teams could benefit from adjusting this mechanism, such that the human or autonomy alternatively receive more control authority in different scenarios. The question then is: how do we know when these scenarios occur? In this paper, we present a method to estimate the performance of human-robot teams using a novel metric called Discrete N-Dimensional Entropy of Behavior (DNDEB). DNDEB utilizes knowledge of a high-performing human-robot team to build a model of how the team should operate. The model is used to predict the human's command. The error between the prediction and actual command is tracked and after a certain number of samples, entropy is estimated. A higher level of entropy corresponds to deviations from the high-performance model, which can be interpreted as poor performance by the human-robot team (e.g., long task time or a collision). Our formulation offers several advantages: it (1) accepts discrete inputs of any size, (2) does not require additional sensors, and (3) is tunable to the specific application. To validate this, we conduct a 15person study where subjects operated a powered wheelchair under three different shared-control paradigms. We find that entropy is higher for cases with longer task durations and cases where there is a collision. Moreover, we use DNDEB thresholds as a mechanism to predict the performance of the human-robot team online and find an average accuracy of 91% with a prescience rate of 72%.

Original languageEnglish (US)
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728140049
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: Nov 3 2019Nov 8 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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