PUB - Publikationen an der Universität Bielefeld

Interactive teaching from a human can be applied to extend the knowledge of a service robot according to novel task demands. This is particularly attractive if it is either inefficient or not feasible to pre-train all relevant object knowledge beforehand. Like in a normal human teacher and student situation it is then vital to estimate the learning progress of the robot in order to judge its competence in carrying out the desired task. While observing robot task success and failure is a straightforward option, there are more efficient alternatives. In this contribution we investigate the application of a recent semi-supervised confidence-based approach to accuracy estimation towards incremental object learning for an inventory assistant robot. We evaluate the approach and demonstrate its applicability in a slightly simplified, but realistic setting. We show that the configram estimation model (CGEM) outperforms standard approaches for accuracy estimation like cross-validation and interleaved test/train error for active learning scenarios, thus minimizing human training effort.