Learning Task-Parameterized Skills from Few Demonstrations

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.

Zhu J, Gienger M., Kober J. Learning Task-Parameterized Skills from Few Demonstrations, IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4063-4070, April 2022.