Human-Robot Deformation Manipulation Skill Transfer: Sequential Fabric Unfolding Method For Robots

Abstract

Deformable object manipulation has been considered a challenging task for robots for its complex dynamics and the infinite dimensional configuration space. Fabric unfolding manipulation takes on critical significance in the textile industry and household services. Accordingly, enabling robots to possess the above-mentioned skill has been confirmed as a crucial and challenging task. In this study, a general framework is developed for transferring human skills to robots in fabric unfolding manipulation. The developed framework comprises two key components (i.e., behavior cloning to learn human unfolding policy and learning from demonstration to transfer unfolding actions). A mixture density network is introduced, with the aim of addressing the multimodality in human policy. Moreover, task parameter weighting is considered during action generalization to adapt to a wide variety of unfolding scenarios. As revealed by the experimental results of this study, the framework can successfully unfold fabrics of different colors and sizes, and its performance can be comparable to human-level operation. Furthermore, the framework also can be applied to garment unfolding, and experiments suggest that it exhibits generalization.