Task-Parameterized Motion Learning With Time-Sensitive Constraints

Teaching motion skills to robots through demonstrations has becomes widely popular. However, precise execution of start-, via-, and end-poses at given times is often not guaranteed, limiting the technology transfer to industrial application. To address this issue, we propose the novel Constrained Expectation Maximization (CEM) algorithm, which enforces time-sensitive constraints (TSC) when learning Gaussian Mixture Models (GMM). Our approach applies to data on Riemannian manifolds and extends to task-parameterized scenarios. We validate CEM against state-of-the-art methods on handwritten data and real robot applications utilizing the KUKA LBR iiwa. By enforcing constraints within the learning process, CEM achieves improved and more efficient reproduction of the demonstration data.