Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, dressing, or cleaning policies. However, the main challenge is correctly generalizing to novel situations, e.g., different surfaces to clean or different arm postures to dress. This article proposes a novel task parameterization and generalization to transport the original robot policy, i.e., position, velocity, orientation, and stiffness. Unlike the state of the art, only a set of points are tracked during the demonstration and the execution, e.g., a point cloud of the surface to clean. We then propose to fit a non-linear transformation that would deform the space and then the original policy using the paired source and target point sets. The use of function approximators like Gaussian Processes allows us to generalize, or transport, the policy from every space location while estimating the uncertainty of the resulting policy due to the limited points in the task parameterization point set and the reduced number of demonstrations. We compare the algorithm's performance with state-of-the-art task parameterization alternatives and analyze the effect of different function approximators. We also validated the algorithm on robot manipulation tasks, i.e., different posture arm dressing, different location product reshelving, and different shape surface cleaning.

翻译：从交互式演示中学习彻底改变了非专家人类教导机器人的方式。通过运动学方式移动机器人即可教会其执行拾取-放置、穿衣或清洁等策略。然而，主要挑战在于如何正确泛化到新情境中，例如不同的待清洁表面或不同的待穿衣手臂姿势。本文提出了一种新颖的任务参数化与泛化方法，用于迁移原始机器人策略（即位置、速度、方向及刚度）。与现有技术不同，本方法在演示与执行过程中仅追踪一组点集（例如待清洁表面的点云）。随后，我们提出拟合一个非线性变换，利用配对的源点集与目标点集来扭曲空间，进而调整原策略。采用高斯过程等函数逼近器，使我们能够从每个空间位置泛化（或迁移）策略，同时通过任务参数化点集的有限点数与少量演示次数估计所得策略的不确定性。我们将算法性能与当前最先进的任务参数化替代方案进行对比，并分析不同函数逼近器的影响。此外，在机器人操作任务（即不同姿势的手臂穿衣、不同位置的货物补货、不同形状的表面清洁）中验证了算法的有效性。