Probabilistic Movement Primitives Based Multi-Task Learning Framework

With the increasing complexity of industrial production and manufacturing tasks, industrial robots are expected to learn intricate operations from simple actions easily and quickly with adaption to dynamic environment. In this paper, a task-parameterized multi-task learning framework is proposed to facilitate rapid learning of operational skills for industrial robots. In this framework, a conditional Probabilistic Movement Primitives (ProMP) is firstly employed to the single-task learning. Using the conditional probability calculation, the extrapolation issue in Learning from Demonstration (LfD) is addressed, enabling robots to learn beyond teaching. Subsequently, the single-task is extended to multi-task scenario by proposing a multi-task learning approach where each single task executes an extrapolation learning. The learned skill can meet the multiple task requirements through an iterative modulation manner. The effectiveness of the proposed framework is validated through both the simulation and a 7-DoF Franka-Emika robot experiment in a predefined task scenario. Furthermore, the outperformance of the proposed method is demonstrated by comparing with the state-of-art movement primitives based learning method.