How Can Everyday Users Efficiently Teach Robots by Demonstration?

Benjamin Li, Maram Sakr, Zhikai Zhang, Haomiao Zhang, HF Machiel Van der Loos, Dana Kulic, Elizabeth Croft

Abstract:

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 hinge upon the quality and quantity of the provided
demonstrations. Our objective is to guide human teachers to provide 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. This approach
seeks to minimize the requisite number of demonstrations by enhancing their distribution throughout the workspace. In
a conducted experiment ( = 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 new task (transfer) without
guidance. The results indicate 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. The
scripts used in this paper are available on GitHub.