Learning Task-Parameterized Skills from Few Demonstrations

Abstract

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Taskparameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations. Videos of the experiments are available at https://sites.google.com/view/ tp-gmm-from-few-demos/