Unsupervised Contextual Task Learning and Recognition for Sharing Autonomy to Assist Mobile Robot Teleoperation

We focus on the problem of learning and recognizing contextual tasks from human demonstrations, aiming to efficiently assist mobile robot teleoperation through sharing autonomy. We present in this study a novel unsupervised contextual task learning and recognition approach, consisting of two phases. Firstly, we use Dirichlet Process Gaussian Mixture Model (DPGMM) to cluster the human motion patterns of task executions from unannotated demonstrations, where the number of possible motion components is inferred from the data itself instead of being manually specified a priori or determined through model selection. Post clustering, we employ Sparse Online Gaussian Process (SOGP) to classify the query point with the learned motion patterns, due to its superior introspective capability and scalability to large datasets. The effectiveness of the proposed approach is confirmed with the extensive evaluations on real data.