1 research outputs found
Quantifying Demonstration Quality for Robot Learning and Generalization
Learning from Demonstration (LfD) seeks to democratize robotics by enabling
diverse end-users to teach robots to perform a task by providing
demonstrations. However, most LfD techniques assume users provide optimal
demonstrations. This is not always the case in real applications where users
are likely to provide demonstrations of varying quality, that may change with
expertise and other factors. Demonstration quality plays a crucial role in
robot learning and generalization. Hence, it is important to quantify the
quality of the provided demonstrations before using them for robot learning. In
this paper, we propose quantifying the quality of the demonstrations based on
how well they perform in the learned task. We hypothesize that task performance
can give an indication of the generalization performance on similar tasks. The
proposed approach is validated in a user study (N = 27). Users with different
robotics expertise levels were recruited to teach a PR2 robot a generic task
(pressing a button) under different task constraints. They taught the robot in
two sessions on two different days to capture their teaching behaviour across
sessions. The task performance was utilized to classify the provided
demonstrations into high-quality and low-quality sets. The results show a
significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the
task performance and generalization performance across all participants. We
also found that users clustered into two groups: Users who provided
high-quality demonstrations from the first session, assigned to the
fast-adapters group, and users who provided low-quality demonstrations in the
first session and then improved with practice, assigned to the slow-adapters
group. These results highlight the importance of quantifying demonstration
quality, which can be indicative of the adaptation level of the user to the
task