Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks

Abstract

To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and timeconsuming. Our thesis is that we can learn user preferences in assembly tasks from demonstrations in a representative canonical task. Inspired by previous work in economy of human movement, we propose to represent user preferences as a linear function of abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn their preference from demonstrations in a canonical task and use the learned preference to anticipate their actions in the actual assembly task without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.