  Moving away from repetitive tasks, robots nowadays demand versatile skills
that adapt to different situations. Task-parameterized 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.
