TY - GEN
T1 - Discrete N-Dimensional Entropy of Behavior
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
AU - Young, Michael
AU - Javaremi, Mahdieh Nejati
AU - Argall, Brenna D.
PY - 2019/11
Y1 - 2019/11
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85081155274&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081155274&partnerID=8YFLogxK
U2 - 10.1109/IROS40897.2019.8968600
DO - 10.1109/IROS40897.2019.8968600
M3 - Conference contribution
AN - SCOPUS:85081155274
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2227
EP - 2233
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 November 2019 through 8 November 2019
ER -