How Can Everyday Users Efficiently Teach Robots by Demonstrations?
Maram Sakr Zhikai Zhang Benjamin Li Haomiao Zhang H. F. Machiel Van der Loos Dana Kulic Elizabeth Croft
Learning from Demonstration (LfD) is a framework that allows lay users to
easily program robots. However, the efficiency of robot learning and the
robot's ability to generalize to task variations hinges upon the quality and
quantity of the provided demonstrations. Our objective is to guide human
teachers to furnish more effective demonstrations, thus facilitating efficient
robot learning. To achieve this, we propose to use a measure of uncertainty,
namely task-related information entropy, as a criterion for suggesting
informative demonstration examples to human teachers to improve their teaching
skills. In a conducted experiment (N=24), an augmented reality (AR)-based
guidance system was employed to train novice users to produce additional
demonstrations from areas with the highest entropy within the workspace. These
novice users were trained for a few trials to teach the robot a generalizable
task using a limited number of demonstrations. Subsequently, the users'
performance after training was assessed first on the same task (retention) and
then on a novel task (transfer) without guidance. The results indicated a
substantial improvement in robot learning efficiency from the teacher's
demonstrations, with an improvement of up to 198% observed on the novel task.
Furthermore, the proposed approach was compared to a state-of-the-art heuristic
rule and found to improve robot learning efficiency by 210% compared to the
heuristic rule.