Learning of Exception Strategies in Assembly Tasks

Abstract

Assembly tasks performed with a robot often fail due to unforeseen situations, regardless of the fact that we carefully learned and optimized the assembly policy. This problem is even more present in humanoid robots acting in an unstructured environment where it is not possible to anticipate all factors that might lead to the failure of the given task. In this work, we propose a concurrent LfD framework, which associates demonstrated exception strategies to the given context. Whenever a failure occurs, the proposed algorithm generalizes past experience regarding the current context and generates an appropriate policy that solves the assembly issue. For this purpose, we applied PCA on force/torque data, which generates low dimensional descriptor of the current context. The proposed framework was validated in a peg-in-hole (PiH) task using Franka-Emika Panda robot.