Paper Info
Title
Teacher-Aware Active Robot Learning
Abstract
This paper investigates Active Robot Learning strategies that take into account the effort of the user in an interactive learning scenario. Most research claims that Active Learning's sample efficiency can reduce training time and therefore the effort of the human teacher. We argue that the performance driven query selection of standard Active Learning can make the job of the human teacher difficult, resulting in a decrease in training quality due to slowdowns or increased error rates. We investigate this issue by proposing a learning strategy that aims to minimize the user's workload by taking into account the flow of the questions. We compare this strategy against a standard Active Learning strategy based on uncertainty sampling and a third strategy being an hybrid of the two. After studying in simulation the validity and the behavior of these approaches, we conducted a user study where 26 subjects interacted with a NAO robot embodying the presented strategies. We reports results from both the robot's performance and the human teacher's perspectives, observing how the hybrid strategy represents a good compromise between learning performance and user's experienced workload. Based on the results, we provide recommendations on the development of Active Robot Learning strategies going beyond robot's performance.
Year
DOI
Venue
2019
10.1109/HRI.2019.8673300
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Keywords
Field
DocType
Training,Robot learning,Task analysis,Standards,Memory management
Robot learning,Interactive Learning,Active learning,Task analysis,Workload,Computer science,Human–computer interaction,Memory management,Robot,Human–robot interaction
Conference
ISSN
ISBN
Citations 
2167-2121
978-1-5386-8555-6
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mattia Racca122.43
Antti Oulasvirta23131217.78
V. Kyrki365261.79