Paper Info
Title
Modeling user expertise for choosing levels of shared autonomy.
Abstract
In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Process (POMDP) to represent a model of the useru0027s expertise in controlling the robot. The POMDP uses observations from the useru0027s actions and from the environment to update the belief of the useru0027s skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. A user study was run to test the performance of our formulation. Users drive a simulated robot through an obstacle-filled map while the POMDP model chooses appropriate macro-action controllers based on the belief state of the useru0027s skill level. The results of the user study show that our model can encapsulate user skill. The results also show that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill.
Year
DOI
Venue
2017
10.1109/ICRA.2017.7989263
ICRA
Field
DocType
Volume
Control theory,Markov process,Partially observable Markov decision process,Autonomy,User modeling,Artificial intelligence,Engineering,Robot,Computer user satisfaction,Mobile robot
Conference
2017
Issue
Citations 
PageRank 
1
2
0.37
References 
Authors
12
2
Name
Order
Citations
PageRank
Lauren Milliken120.37
Geoffrey A. Hollinger233427.61