Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Tasks

TL;DR

This paper introduces a two-stage clustering method to infer human preferences at multiple resolutions in assembly tasks, improving prediction accuracy and task efficiency in robot-assisted environments.

Contribution

It presents a novel two-stage approach for learning human preferences at different resolutions, enhancing action prediction and task support in complex assembly tasks.

Findings

Improved prediction of human actions via cross-validation.

Enhanced task execution efficiency in online experiments.

Successful application in real-world robot-assisted IKEA assembly.

Abstract

To effectively assist human workers in assembly tasks a robot must proactively offer support by inferring their preferences in sequencing the task actions. Previous work has focused on learning the dominant preferences of human workers for simple tasks largely based on their intended goal. However, people may have preferences at different resolutions: they may share the same high-level preference for the order of the sub-tasks but differ in the sequence of individual actions. We propose a two-stage approach for learning and inferring the preferences of human operators based on the sequence of sub-tasks and actions. We conduct an IKEA assembly study and demonstrate how our approach is able to learn the dominant preferences in a complex task. We show that our approach improves the prediction of human actions through cross-validation. Lastly, we show that our two-stage approach improves the efficiency of task execution in an online experiment, and demonstrate its applicability in a real-world robot-assisted IKEA assembly.